FINGERPRINT RECOGNITION SYSTEM

Believing of the importance of biometrics in this research, we have presented a fused system that depends upon multimodal biometric system traits face, iris, and fingerprint, achieving higher performance than the unimodal biometrics. The proposed system used Local Binary Pattern with Variance histogram (LBPV) for extracting the preprocessed features. Canny edge detection and Hough Circular Transform (HCT) were used in the preprocessing stage while, the Combined Learning Vector Quantization classifier (CLVQ) was used for matching and classification. Reduced feature dimensions are obtained using LBPV histograms which are the input patterns for CLVQ producing the classes as its outputs. The fusion process was performed at the decision level based on majority voting algorithm of the output classes resulting from CLVQ classifier. The experimental results indicated that the fusion of face, iris, and fingerprint has achieved higher genuine acceptance recognition rate (GAR) 99.50% with mini...

Biometric recognition systems have advanced significantly in the last decade and their use in specific applications will increase in the near future. The ability to conduct meaningful comparisons and assessments will be crucial to successful deployment and increasing biometric adoption. The best modality used as unimodal biometric systems are unable to fully address the problem of higher recognition rate. Multimodal biometric systems are able to mitigate some of the limitations encountered in unimodal biometric systems, such as non-universality, distinctiveness, non-acceptability, noisy sensor data, spoof attacks, and performance. More reliable recognition accuracy and performance are achievable as different modalities were being combined together and different algorithms or techniques were being used. The work presented in this paper focuses on a bimodal biometric system using face and fingerprint. An image enhancement technique (histogram equalization) is used to enhance the face ...

Biometrics is a readily available metric that is playing a vital role in face recognition. There are many domain areas for the features extractionsuch as frequency measures, geometrical measures and statistical measures. In the proposed Face Recognition System (FRS)the extraction of features are done using frequency and statistics measures. The FFT,DCT, variation, standard deviation, mean, median and entropy are the five features extracted from the ORL dataset for recognition process. A local normalization technique is applied to the extracted features. The training and testing are performed with normalized features using Back PropagationNeural Network (BPNN).

Cloud computing is a promising new technology that is revolutionizing the old Internet computing paradigm and the entire IT sector. Customers and many firms, such as Apple, Google, Facebook, and Amazon, are increasingly adopting mobile cloud computing technology. Because of the rapid advancements in wireless access technologies, cloud computing is expected to grow in the mobile environment. These mobile apps are based on mobile cloud computing principles and methodologies. Users can store their data remotely and access high-quality on-demand cloud apps in the Mobile Cloud environment, eliminating the need to purchase and maintain local hardware and software. These data security vulnerabilities can be remedied with sound data processing. While storing data on the cloud, the cloud service provider can secure the data by using encryption and decryption techniques. This paper suggested encryption and decryption methods for safeguarding data in the cloud. Due to the encrypted format, confidential data cannot be accessed by an unauthorized person or machine.

The widespread popularity of the Internet and the fast developments in computer technologies influence the expansion of electronic data exchange and digital communications. Consequently, digital audio communication is used in daily life activities such as banking, commerce, e-learning, military, education and politics. As a result, a huge amount of critical audio data is exchanged everyday over shared and open networks. In consequence of the rapid growth of data communications and digital audio, the issue of providing a high level of audio security becomes a foremost importance. Chaotic maps have been used recently in cryptography for large scale data encryption such as text, image, video and audio data, due to their strong properties such as sensitivity to changes in system control parameters and initial conditions, pseudo-randomness and aperiodicity. This paper has been presented a chaos-based audio confusion and diffusion system. A symmetric block audio encryption approach have b...

This paper presents new techniques for designing a simple and reliable multifeatured biometric system based on a single trait source. First, a one-toone relationship between the feature's edge and its associated angle is utilized after extracting the contrast feature using the gray-level co-occurring matrix (GLCM) method. Secondly, the classifying stage is modified to process one-dimensional vectors rather than the whole feature's template. That means whatever the template size is, the matching operation is always processing a one-dimensional vector called a mean-feature vector which requires low storage and less computation complexity. Finally, for comparison purposes, the performances of the three biometric systems are calculated for recognizing 170 subjects taken from four facial databases. These comparisons are made using three error distance measurements. The recognition rates of the angle-based feature were very competitive to the regular edge-based results; however, the overall recognition accuracy is highly improved after fusing the decision of the two unibiometric systems using the Logic-OR operator. The fused system performance was satisfactory and it shows that the decision fusion of the single source trait based multifeatured system has promising performance represented by accuracy improvement, low storage, and low matching time.

Multimedia security has been extensively used in content protection, image authentication, data hiding and signal encryption. Similarly, transmission of biomedical data or information remotely for healthcare applications should be secure. One of the important medical signals that need to be transmitted to healthcare centers is the Electrocardiogram (ECG) signal. This paper is concerned mainly with ECG signal encryption for security applications. The paper presents three cryptosystems for ECG signal encryption based on the fusion of ECG signals with other masking signals that are rich in activities such as speech signals. The common thread between these cryptosystems is the operation on sample values of the ECG signal rather than adopting encoding and decoding schemes, and this saves much time and is more immune to both noise and hacking scenarios. The proposed cryptosystems are compared to the encryption technique that uses 1-D logistic map. The performances of the proposed cryptosystems are evaluated through simulation experiments in terms of histogram, structural similarity index, Signal-to-Noise Ratio (SNR), log-likelihood ratio, spectral distortion and correlation coefficient. It is clear from the experiments that the utilization of more levels of encryption increases the security.

Superresolution (SR) is a method of enhancing image resolution by combining information from multiple images. Two main processes in superresolution are r egist ation and image reconstruction. Both of these processes greatly affect the quality image of the s uperresolution. Accurate registration is required t o obtain high-resolution image quality. This research propos e a collaboration between Phase-Based Image Matchin g (PBIM) registration, and reconstruction using Struc ture Adaptive Normalized convolution algorithm (SANC) and Projection Onto Convex sets algorithm (P OCs). PBIM was used to estimate translational registration stage. We used the function fitting ar ound the peak point, to obtain sub pixel accurate s hift. The results of this registration were used for reconstr uction. Three registration method and two reconstru ction algorithms have been tested to obtain the most appr opriate collaboration by measuring the value of Pea k Signal to Noise Ratio (PSNR). The result show...

Location-Based Services (LBSs) are increasingly popular in today's society. LBSs are used in a broad variety of applications such as social networks with location sharing. LBSs can help people enjoying a convenient life and has attracted considerable interest recently. However, the privacy issues of LBS are still challenging today. Usually LBS Providers (LBSPs) offer user privacy protection statement to assure users that their private location information would not be given away. However, many LBSs run-on third-party cloud infrastructures. So, the privacy is still in a big problem. While a large number of privacy-preserving solutions for LBS have been proposed, nowadays most of these solutions do not consider the fact that LBS is typically cloud-based. Aiming at the challenges, in this paper, we present a new efficient Privacy Preserving LBS Query scheme based on Fully Homomorphic Encryption (PPQ_FHE). In PPQ_FHE scheme, the LBSP's data are outsourced to the cloud server in an encrypted manner, and a registered user can get accurate LBS query results without divulging his/her location information to the LBSP and cloud Server Provider (CSP). Specifically, based on improving FHE over Advanced Encryption Standard (AES). We provide a security analysis to show that PPQ_FHE scheme preserves privacy in the presence of different threats. PPQ_FHE analysis and evaluation results demonstrate that proposed PPQ_FHE scheme can preserve the privacy of the LBS users in an efficient and secure way. The results prove the feasibility and efficiency of PPQ_FHE scheme in terms of query's response time, query accuracy, throughput and query overhead.

Face alignment is a computer vision technology for identifying the geometric structure of human faces in digital images. Given the location and size of a face, it automatically determines the shape of the face components such as eyes and nose. A face alignment program typically operates by iteratively adjusting a ▶ deformable models, which encodes the prior knowledge of face shape or appearance, to take into account the low-level image evidences and find the face that is present in the image.

In fingerprint-based systems, the size of databases increases considerably with population growth. In developing countries, because of the difficulty in using a central system when enlisting voters, it often happens that several regional voter databases are created and then merged to form a central database. A process is used to remove duplicates and ensure uniqueness by voter. Until now, companies specializing in biometrics use several costly computing servers with algorithms to perform large-scale deduplication based on fingerprints. These algorithms take a considerable time because of their complexity in O (n 2), where n is the size of the database. This article presents an algorithm that can perform this operation in O (2n), with just a computer. It is based on the development of an index obtained using a 5 * 5 matrix performed on each fingerprint. This index makes it possible to build clusters of O (1) in size in order to compare fingerprints. This approach has been evaluated using close to 11 4000 fingerprints, and the results obtained show that this approach allows a penetration rate of less than 1%, an almost O (1) identification, and an O (n) deduplication. A base of 10 000 000 fingerprints can be deduplicated with a just computer in less than two hours, contrary to several days and servers for the usual tools.

Cancelable biometrics is an important biometric template protection scheme. With no a priori image pre-alignment, alignment-free cancelable fingerprint templates do not suffer from inaccurate singular point detection. In this paper, we develop a blind system identification approach to the design of alignment-free cancelable fingerprint templates. The binary string, derived from quantized pair-minutiae vectors, is to be secured and its frequency samples act as the input to the proposed algorithm. Motivated by the identifiability of the source signal in blind system identification, we propose to protect the binary string's frequency samples, which are treated as the source input, by countering or dissatisfying the identifiability condition so that they cannot be recovered from the output complex vector (transformed template). The proposed transform is irreversible because when the identifiability condition

Recent findings confirm that biometric templates derived from electroencephalography (EEG) signals contain sensitive information about registered users, such as age, gender, cognitive ability, mental status and health information. Existing privacypreserving methods such as hash function and fuzzy commitment are not cancelable, where raw biometric features are vulnerable to hill-climbing attacks. To address this issue, we propose the PolyCosGraph, a system based on Polynomial transformation embedding Cosine functions with Graph features of EEG signals, which is a privacy-preserving and cancelable template design that protects EEG features and system security against multiple attacks. In addition, a template corrupting process is designed to further enhance the security of the system, and a corresponding matching algorithm is developed. Even when the transformed template is compromised, attackers cannot retrieve raw EEG features and the compromised template can be revoked. The proposed system achieves the authentication performance of 1.49% EER with a resting state protocol, 0.68% EER with a motor imagery task, and 0.46% EER under a watching movie condition, which is equivalent to that in the non-encrypted domain. Security analysis demonstrates that our system is resistant to attacks via record multiplicity, preimage attacks, hill-climbing attacks, second attacks and brute force attacks.

Fingerprint authentication techniques have been employed in various Internet of Things (IoT) applications for access control to protect private data, but raw fingerprint template leakage in unprotected IoT applications may render the authentication system insecure. Cancelable fingerprint templates can effectively prevent privacy breaches and provide strong protection to the original templates. However, to suit resource-constrained IoT devices, oversimplified templates would compromise authentication performance significantly. In addition, the length of existing cancelable fingerprint templates is usually fixed, making them difficult to be deployed in various memory-limited IoT devices. To address these issues, we propose a novel length-flexible lightweight cancelable fingerprint template for privacy-preserving authentication systems in various resource-constrained IoT applications. The proposed cancelable template design primarily consists of two components: 1) length-flexible partial-cancelable feature generation based on the designed reindexing scheme and 2) lightweight cancelable feature generation based on the designed encoding nested difference XOR scheme. Comprehensive experimental results on public databases FVC2002 DB1-DB4 and FVC2004 DB1-DB4 demonstrate that the proposed cancelable fingerprint template achieves equivalent authentication performance to state-of-theart methods in IoT environments, but our design substantially reduces template storage space and computational cost. More importantly, the proposed length-flexible lightweight cancelable template is suitable for a variety of commercial smart cards (e.g., C5-M.O.S.T. Card Contact Microprocessor Smart Cards CLXSU064KC5). To the best of our knowledge, the proposed method is the first length-flexible lightweight, high-performing cancelable fingerprint template design for resource-constrained IoT applications.

The main purpose of the classification of fingerprints is to devise a formula by which a given collection of fingerprints can be tracked and registered. To accelerate the system for classifying fingerprints, it is necessary to utilize fingerprint image characteristics and avoid the different fingerprint forms arising from fingerprint rotation. This paper presents a simple, new approach to the extraction of characteristics from fingerprint images. The proposed method demonstrates that, for a given image, the features remain constant even after being subjected to a wide range of rotations; thus, it creates an array of characteristics which can be used to identify a person from their fingerprint. To achieve this goal, a basic hit-and-miss operation with different structural components is used to detect and count various features in the fingerprint picture; these features are directly identified based on the texture of the fingerprint. The chosen features are used to index the finger im...

Pseudo-Random Number Generators (PRNGs) play an important role in many cryptographic applications. Many network security algorithms and protocols that are based on cryptography also require random or pseudorandom numbers at certain points. PRNG is an algorithm that is used to generate a random sequence that has the same behavior of truly random numbers. Each generated number should not depend on the previously generated numbers, and thus it is not possible to predict such numbers. However, not all PRNGs are suitable for cryptographic applications. Therefore, various statistical tests can be applied to the obtained sequence to evaluate and compare it with truly random sequences. A PRNG that passes all statistical tests can be considered as a statistically secure PRNG. Based on some properties of chaos, such as randomness, unpredictability, and high sensitivity to the secret key, we proposed a new PRNG. The proposed PRNG is based on a modified chaotic map proposed in [20], and a new lookup table, which is created in such way that the stored numbers will not be duplicated in the same row, column, or diameter. The proposed map and lookup table are used to produce non-linear and non-invertible functions, which are the main targets of any secure PRNG. As a result, the behavior of the proposed PRNG simulates the behavior of TRNGs. The obtained results from the cryptographic analysis and the standard statistical National Institute of Standards and Technology (NIST) tests indicate the robustness of the proposed PRNG. Furthermore, it is robust against known cryptographic attacks, and it has a strong non-linearity compared to the other systems. A comparison study of efficiency in terms of both speed performance and robustness against cryptanalysis was done, and the results demonstrate the superiority of the proposed algorithm.

In this paper provides the design method of wireless fingerprint attendance system based on ZigBee technology using LAN networking. The system includes terminal fingerprint acquisition module and attendance management module through computer. It can realize automatically such functions as information acquisition of fingerprint, processing, wireless transmission, fingerprint matching, and attendance management. Considering the fact and topology of ZigBee network, a fingerprint acquisition module and a wireless alarm module were designed by using the fingerprint sensor and module respectively. The whole system was implemented wireless alarm through messages and internet in the GSM web. All HODs PCs are worked as workstation and LAN connected with the Principal office. In order to achieve the simple and high real-time system, it realized lowcost and high-performance wireless fingerprint attendance function, which provided a new wireless fingerprint attendance system for enterprises and institutions.

The fingerprint is the most widely used technology for identification or authentication systems, which can be known as fingerprint authentication systems (FAS). In addition to providing security, the fingerprint is also easy to use, very reliable and has a high accuracy for identity recognition. FAS is still exposed to security attacks because fingerprint information is unencrypted. Therefore, fingerprint information requires protection known as fingerprint template protection (FTP). This paper aims to provide an organized literature on FTP. Three research questions were formulated to guide the literature analysis. First, this analysis focuses on the types of FTP schemes; second, the metrics used for evaluating the FTP schemes; and finally, the common datasets used for evaluating the FTP schemes. The latest information and references are analysed and classified based on FTP methods and publication year to obtain information related to the development and application of FTP. This study mainly surveyed 62 documents reported on FTP schemes between the year 2000 and 2017. The results of this survey can be a source of reference for other researchers in finding literature relevant to the FTP.

One possible attack in the biometric system is the template stored in the database. This attack can cause Biometric template information leakage, thus pose a serious privacy security threat. Most available template protection techniques fail to meet all the desired requirements of practical biometric systems such as diversity, revocability, security, and performance matching accuracy. This paper aims to review the various fingerprint template protection (ftp) approaches that have been proposed by researchers in recent decades. Some of the proposed schemes are standard encryption, biometric cryptosystem, template transformation, hybrid scheme and homomorphic encryption.

This paper introduced some new techniques to extract the palmprint features based on fractal codes and fractal dimension. Four new techniques based on fractal, namely FDFC (fractal dimension from fractal codes), FDIFC (fractal dimension image from fractal code), DIFC (distance image from fractal code), and AIFC (Angle image from fractal code) are proposed.. The main idea of these methods is the forming palmprint image representation by using fractal code parameters. There are three parameters of fractal codes are used namely position of range block, position of domain block, and size of range block. Those methods have been applied for palmprint verification system. The system has been tested by using database of 1050 palmprint images, are generated from 5 samples from each of the 210 persons randomly selected. Experiment results show that our proposed methods can achieve an acceptable accuracy rate.

To overcome the problem related to cloud computing such as high latency and to increase the efficiency of data processing, Fog computing was proposed and implemented close to IoT devices. This structure helps these devices to meet the requirements of location awareness and low latency, which can not be met by cloud computing. Many benefits, applications, and security and privacy issues were discussed in the literature for Fog computing. This paper provides an overview of all these aspects to provide the reader with a holistic understanding of Fog computing. More focus is given to the security and privacy issues in our discussion since these issues may hinder organizations from implementing Fog computing. The findings of this paper show that a few studies that discussed empirical findings of using Fog computing and many questions related to security and privacy issues are ye to be answered in future research.

The fingerprint is the most widely used technology for identification or authentication systems, which can be known as fingerprint authentication systems (FAS).In addition to providing security, the fingerprint is also easy to use, very reliable and has a high accuracy for identity recognition. FAS is still exposed to security attacks because fingerprint information is unencrypted.Therefore, fingerprint information requires protection known as fingerprint template protection (FTP).This paper aims to provide an organized literature on FTP.Three research questions were formulated to guide the literature analysis.First, this analysis focuses on the types of FTP schemes; second, the metrics used for evaluating the FTP schemes; and finally, the common datasets used for evaluating the FTP schemes. The latest information and references are analysed and classified based on FTP methods and publication year to obtain information related to the development and application of FTP.This study mai...

This paper introduced some new techniques to extract the palmprint features based on fractal codes and fractal dimension. Four new techniques based on fractal, namely FDFC (fractal dimension from fractal codes), FDIFC (fractal dimension image from fractal code), DIFC (distance image from fractal code), and AIFC (Angle image from fractal code) are proposed.. The main idea of these methods is the forming palmprint image representation by using fractal code parameters. There are three parameters of fractal codes are used namely position of range block, position of domain block, and size of range block. Those methods have been applied for palmprint verification system. The system has been tested by using database of 1050 palmprint images, are generated from 5 samples from each of the 210 persons randomly selected. Experiment results show that our proposed methods can achieve an acceptable accuracy rate.

Social network Sites (SNS) are the major source for social capital[6], now a day’s dynamic mining [2] is a part of its activity for many organizations. Inter estingly on the other end we can see that most of t he users are having more than one registration on social net works [6] has been associated with two fold effects in positive and negative shades. Many young users are using these networks for adolescents [10] and for downloading free entertainment stuff, which may lea ds to lot of fake user registrations. Just receivin g invitation to their emails, creates some of the use r profiles and it then pulls the userdata of the ma il account. The profile created may not be correct and this was copied [11] may lead to false or redundant informa tion on the network domain. This became a common problem for all organizations, websites who are associated with the SNS to find many facts like number of user s registered and tracking any person also sometime i may leads to ambiguity problem. ...

Automatic Fingerprint authentication for personal identification and verification has received considerable attention over the past decades among various biometric techniques because of the distinctiveness and persistence properties of fingerprints. Now fingerprints are set to explode in popularity as they are being used to secure smart phones and to authorize payments in online stores. The main objective of this paper is to review the extensive research work that has been done over the past decade and discuss the various approaches proposed for fingerprint matching. The proposed methods were based on 2D correlation in the spatial and frequency domains, Artificial Neural Networks, Hough transform, Fourier transform, graphs, local texture, ridge geometry etc. All these different techniques have their pros and cons. This paper also provides the performance comparison of several existing methods proposed by researchers in fingerprint matching.

Fingerprint registration and verification is an active area of research in the field of image processing. Usually, fingerprints are obtained from sensors; however, there is recent interest in using images of fingers obtained from digital cameras instead of scanners. An unaddressed issue in the processing of fingerprints extracted from digital images is the angle of the finger during image capture. To match a fingerprint with 100% accuracy, the angles of the matching features should be similar. This paper proposes a rotation and scale-invariant decision-making method for the intelligent registration and recognition of fingerprints. A digital image of a finger is taken as the input and compared with a reference image for derotation. Derotation is performed by applying binary segmentation on both images, followed by the application of speeded up robust feature (SURF) extraction and then feature matching. Potential inliers are extracted from matched features by applying the M-estimator....

Biometric advancements have the potential to provide more robust security and authentication approaches compared to conventional password-based security measures. When compared to knowledge-based security, biometrics relies on what makes you unique rather than what you know. Biometrics offers promising opportunities to enhance security but also comes with its own set of challenges. Successful implementation requires attention to these possible flaws and a rise in user approval. This article explores the potential benefits of biometric security in an effort to ascertain how broadly adopted it is. It analyses the correlation between user approval and their opinion of the feature's usefulness and accessibility. It also examines crucial acceptability elements like social, organizational, and cost considerations. Biometric systems are a type of pattern recognition technology that may identify a person by vetting their claimed physiological and/or behavioural traits. The following concept of a biometric system can be very broad, which is problematic when thinking about the specifics of contemporary biometric applications. The use of biometrics has the potential to greatly improve upon the security of current methods of authentication and identification. The broad use of biometrics solutions, in fact, has significant ramifications for the way we think about the individual's place in society. However, certain factors and limits are taken into account in the context of the application area, as is the case with any technology. Further progress will be made as scientists investigate multiple, interconnected topics. This paper addresses technical and engineering concerns while highlighting potential applications of contemporary biometric technologies.

Privacy-enhancing technologies are technologies that implement fundamental data protection principles. With respect to biometric recognition, different types of privacy-enhancing technologies have been introduced for protecting stored biometric data which are generally classified as sensitive. In this regard, various taxonomies and conceptual categorizations have been proposed and standardization activities have been carried out. However, these efforts have mainly been devoted to certain sub-categories of privacy-enhancing technologies and therefore lack generalization. This work provides an overview of concepts of privacy-enhancing technologies for biometrics in a unified framework. Key aspects and differences between existing concepts are highlighted in detail at each processing step. Fundamental properties and limitations of existing approaches are discussed and related to data protection techniques and principles. Moreover, scenarios and methods for the assessment of privacy-enhancing technologies for biometrics are presented. This paper is meant as a point of entry to the field of biometric data protection and is directed towards experienced researchers as well as non-experts.

Believing of the importance of biometrics in this research, we have presented a fused system that depends upon multimodal biometric system traits face, iris, and fingerprint, achieving higher performance than the unimodal biometrics. The proposed system used Local Binary Pattern with Variance histogram (LBPV) for extracting the preprocessed features. Canny edge detection and Hough Circular Transform (HCT) were used in the preprocessing stage while, the Combined Learning Vector Quantization classifier (CLVQ) was used for matching and classification. Reduced feature dimensions are obtained using LBPV histograms which are the input patterns for CLVQ producing the classes as its outputs. The fusion process was performed at the decision level based on majority voting algorithm of the output classes resulting from CLVQ classifier. The experimental results indicated that the fusion of face, iris, and fingerprint has achieved higher genuine acceptance recognition rate (GAR) 99.50% with mini...

Cancelable and hybrid biometric cryptosystems are two techniques used to offer protection against the security and privacy challenges faced by users of biometric authentication systems. The main objective of this paper is to present a critical review of current and emerging trends as well as open research issues in cancellable and hybrid biometric systems. The study examines cancelable biometrics under two main categories, namely non-invertible transformation and biometric salting. It also explores hybrid cryptosystems as means of providing improved template security and user privacy. The review focusses on the modes of operation, performance accuracy, security and privacy of various types of cancellable and hybrid biometric cryptosystems. It also provides a more comprehensive survey of latest research works in cancellable and hybrid biometric cryptosystems than existing review papers in these fields. The paper will provide readers with up-to-date information on current directions and open research issues in cancelable and hybrid biometric cryptosystems.

Today, biometrics is a research field in full expansion, several identification and verification systems are now being developed, however their performance is inadequate to meet the growing needs of security. The use of a single biometric modality decreases, in most cases, the reliability of these systems, which prompted us to combine several modality. This article provides a complementary aspect to the improved performance of biometric systems namely the protection of privacy. Techniques have been proposed in the last decade on the biometric template protection. Include BioHashing that wants a generalization of biometrics a hash function to protect the biometric data. The main objective is the protection of privacy. This paper presents state of the art specific algorithms, which are illustrated with recent work by researchers in this field.

Background/Objective: A computer based biological recognition system implementation for gender identification by using digitally acquired fingerprint through an optical fingerprint sensor. Methods/Statistical Analysis: Gender identification from fingerprint using Discrete Wavelet Transform and Principle Component Analysis for extracting both spatial domain features(SDF) and frequency domain features(FDF), and is combined together for accurate gender identification through a comparison with a set of sample fingerprints of different sex, age, ethnicity, etc. Findings: The results of this approach has revealed that the accuracy of the system depends on the database fingerprint images, bigger the database, better the results, wider the age range of the fingerprints in database, better the results, it also reveals, that unlike the traditional fingerprint ridge to valley thickness ratio methods, the computer based system is more accurate, faster and involves much less human effort, thereby avoiding human errors. Applications/Improvements: This system has a wide scope in anthropology, criminal investigations, personal identification systems like AADHAAR, etc. For large scale applications, a separate algorithm has to be developed for storing the feature vectors of the large amount of database images and significantly improve the processing speed.

Most of system uses Biometric to identify persons .So one can use any feature such as it may be face, fingerprint ,hand retina etc. In this paper we focus on fingerprint recognition. Input image is scanned and then pre-process to extract feature using different algorithm. In this way by using fingerprint as input image we process on this image and find out the person is authorized or not.

For most of us now, life is incomplete if living without mobile phones. This is because mobile phones are like a necessity to many people nowadays. Statistics have shown that more than seven billion people in the world are having these devices in 2015. This also means 97% of the human world populations are actually mobile phone users. Besides, more than 50% of the mobile phone users are using smarts phones which are capable of downloading a lot of mobile application systems (apps). It is estimated that more than 200 million apps are being downloaded in 2007 and this number is believed to be growing. Unfortunately, many of these apps involve the transfer of important and confidential personal data or business information. How to ensure this sensitive information is well protected from being stolen or misused by unauthorized parties? One of the ways to secure this communication is to properly control the access to the system by strengthening the user authentication. Thus, this paper f...

with identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing different number of unregistered minutiae points. The proposed filterbased algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length Finger Code. The fingerprint matching is based on the Euclidean distance between the two corresponding Finger Codes and hence is extremely fast.

Believing of the importance of biometrics in this research, we have presented a fused system that depends upon multimodal biometric system traits face, iris, and fingerprint, achieving higher performance than the unimodal biometrics. The proposed system used Local Binary Pattern with Variance histogram (LBPV) for extracting the preprocessed features. Canny edge detection and Hough Circular Transform (HCT) were used in the preprocessing stage while, the Combined Learning Vector Quantization classifier (CLVQ) was used for matching and classification. Reduced feature dimensions are obtained using LBPV histograms which are the input patterns for CLVQ producing the classes as its outputs. The fusion process was performed at the decision level based on majority voting algorithm of the output classes resulting from CLVQ classifier. The experimental results indicated that the fusion of face, iris, and fingerprint has achieved higher genuine acceptance recognition rate (GAR) 99.50% with mini...

Pseudo-Random Number Generators (PRNGs) play an important role in many cryptographic applications. Many network security algorithms and protocols that are based on cryptography also require random or pseudorandom numbers at certain points. PRNG is an algorithm that is used to generate a random sequence that has the same behavior of truly random numbers. Each generated number should not depend on the previously generated numbers, and thus it is not possible to predict such numbers. However, not all PRNGs are suitable for cryptographic applications. Therefore, various statistical tests can be applied to the obtained sequence to evaluate and compare it with truly random sequences. A PRNG that passes all statistical tests can be considered as a statistically secure PRNG. Based on some properties of chaos, such as randomness, unpredictability, and high sensitivity to the secret key, we proposed a new PRNG. The proposed PRNG is based on a modified chaotic map proposed in [20], and a new l...

Internet is the base of web based communication. The electronic transfer is only possible when both the parties recognize each other. In other words, only authorize parties can make electronic (money) transfer over the Internet; the security is the main concern here. Social Networking is a basic need of the generation, today. Users share their private information in the form of text or media like: Image, Audio and Video over the Social Networking websites. The main drawback of social networking is: faker can easily create fake accounts by representing personal information (like name and profile picture) of others, because it is easy to copy from the browser directly. Now, the question arises: "What is the Unique Digital Identity of user over the Social Network?” This research paper discusses about the security concerns over Social Network. The researched analysis provides a solution to the problems regarding privacy of a Digital Identity by using Digital Watermarking, where Dig...

Cloud computing has developed from various technologies such as autonomic computing, virtualization, grid computing, and other technologies, and the secure storage is essential and important for it due to it provides virtualized resources on Internet. Therefore, Data owner must encrypt his documents locally before uploading it in the public cloud storage to prevent unauthorized access to his data. Sometimes, the data owner wants to share some of his encrypted documents that stored in the cloud with other authorized users, so, he must send the secret key for each document for all authorized users, but this way has many limitations due to the difficulty of key management and key distribution. To overcome drawback of this approach we proposed system to generate a single key used for multiple number of documents and users depending on two techniques asymmetric cryptography and symmetric cryptography. Asymmetric cryptography used the IBC of the data owner to generate his private key and split the private key into two parts give one part to all authorized users and the other part send to Semi-Trusted Third Party (STTP). While symmetric cryptography used by combined secret key with the encrypted file properties and decrypted the result by the public key of the data owner by using asymmetric cryptography (RSA algorithm). Finally, many results were obtain from implementing the proposed system, among these results; the data owner could add or revoke any user without change the master secret key, also the data owner not need to share multi keys with authorized users. As well as, the system overcame the problem on difficulty of searching over encrypted data through encryption key in a public cloud.

Pseudo-Random Number Generators (PRNGs) play an important role in many cryptographic applications. Many network security algorithms and protocols that are based on cryptography also require random or pseudorandom numbers at certain points. PRNG is an algorithm that is used to generate a random sequence that has the same behavior of truly random numbers. Each generated number should not depend on the previously generated numbers, and thus it is not possible to predict such numbers. However, not all PRNGs are suitable for cryptographic applications. Therefore, various statistical tests can be applied to the obtained sequence to evaluate and compare it with truly random sequences. A PRNG that passes all statistical tests can be considered as a statistically secure PRNG. Based on some properties of chaos, such as randomness, unpredictability, and high sensitivity to the secret key, we proposed a new PRNG. The proposed PRNG is based on a modified chaotic map proposed in [20], and a new l...

Fingerprint evidence is undoubtedly the most reliable and acceptable evidence till date in the court of law. Fingerprints are obtained at the site of crime and in many old monuments and in excavated things. Estimating the gender of fingerprints is an emerging field and many methods using the fingerprint physical features like the ridge count and the ridge thickness have been used so far. Due to the immense potential of fingerprints as an effective method of identification an attempt has been made in the present work to analyze their correlation with gender of an individual using frequency domain technique and a pattern recognition technique. The combined processing has provided better results. This paper aims in using 2D- Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) combined to classify gender using an obtained fingerprint. The minimum distance method was used as a classifier. Fingerprints of 200 males and 200 females belonging to the various age groups we...

Wireless techniques utilized in industrial applications face significant challenges in preventing noise, collision, and data fusion, particularly when wireless sensors are used to identify and classify fault in real time for protection. This study will focus on the design of integrated wireless fault diagnosis system, which is protecting the induction motor (IM) from the vibration via decrease the speed. The filtering, signal processing, and Artificial Intelligent (AI) techniques are applied to improve the reliability and flexibility to prevent vibration increases on the IM. Wireless sensors of speed and vibration and card decision are designed based on the wireless application via the C++ related to the microcontroller, also, MATLAB coding was utilized to design the signal processing and the AI steps. The system was successful to identify the misalignment fault and dropping the speed when vibrations rising for preventing the damage may be happen on the IM. The vibration value reduc...

Today, biometrics are the preferred technologies for person identification, authentication, and verification cutting across different applications and industries. Sadly, this ubiquity has invigorated criminal efforts aimed at violating the integrity of these modalities. Our study presents a multi-biometric cancellable scheme (MBCS) that exploits the proven utility of deep learning models to fuse multi-exposure fingerprint, finger vein, and iris biometrics by using an Inspection V3 pre-trained model to generate an aggregate tamper-proof cancellable template. To validate our MBCS, we employed an extensive evaluation including visual, quantitative, and qualitative assessments as well as complexity analysis where average outcomes of 99.158%, 24.523 dB, 0.079, 0.909, 59.582 and 23.627 were recorded for NPCR, PSNR, SSIM, UIQ, SD and UACI respectively. These quantitative outcomes indicate that the proposed scheme compares favourably against state-of-the-art methods reported in the literature. To further improve the utility of the proposed MBCS, we are exploring its refinement to facilitate generation of cancellable templates for real-time biometric applications in person authentication at airports, banks, etc.

The fingerprint is the most widely used technology for identification or authentication systems, which can be known as fingerprint authentication systems (FAS).In addition to providing security, the fingerprint is also easy to use, very reliable and has a high accuracy for identity recognition. FAS is still exposed to security attacks because fingerprint information is unencrypted.Therefore, fingerprint information requires protection known as fingerprint template protection (FTP).This paper aims to provide an organized literature on FTP.Three research questions were formulated to guide the literature analysis.First, this analysis focuses on the types of FTP schemes; second, the metrics used for evaluating the FTP schemes; and finally, the common datasets used for evaluating the FTP schemes. The latest information and references are analysed and classified based on FTP methods and publication year to obtain information related to the development and application of FTP.This study mai...

The field of biometrics has evolved tremendously for over the last century. Yet scientists are still continuing to come up with precise and efficient algorithms to facilitate automatic fingerprint recognition systems. Like other applications, an efficient feature extraction method plays an important role in fingerprint based recognition systems. This paper proposes a novel feature extraction method using minutiae points of a fingerprint image and their intersections. In this method, initially, it calculates the ridge ends and ridge bifurcations of each fingerprint image. And then, it estimates the minutiae points for the intersection of each ridge end and ridge bifurcation. In the experimental evaluation, we tested the extracted features of our proposed model using a support vector machine (SVM) classifier and experimental results show that the proposed method can accurately classify different fingerprint images.

In today's complex world, due to the increasing use of web-based programs, smart phones and social networks, production rate is increasing constantly as well as volume of data. Also, companies try to provide their services with more features in order to stay ahead of their competitors. That increases the production of information by the users too. In this case, it seems that moving from rational databases to non-relational databases in a necessity. With regard to the various of use cases and their requirements, also the primary features of NoSQL databases, choosing the most appropriate NoSQL database can be big concern of developers. The right selection of NoSQL databases will avoid to wasting time, money, and energy. On this paper, we propose a general and structured method to help developers, customers, and managers to evaluate NoSQL databases in the right way and facilitate the process of decision making to select one of them.