FINGERPRINT RECOGNITION SYSTEM

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.

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.

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.

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...

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 ...

Speech processing is considered as crucial and an intensive field of research in the growth of a robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet-based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is
decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying
window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of
speech signals.

Resumen. En la toma de impresiones dactilares es normal que se empleen fichas decadactilares. Cuando se necesita manipular esta información para automatizar procesos de identificación, es necesario digitalizar y separar las imágenes de cada huella para realizarles análisis y extracción de características, de manera iterativa. Este proceso se conoce como segmentado de huellas en fichas decadactilares, y es el tema de este trabajo, donde se expone en detalle un método empírico novedoso para realizarlo.

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.

In this paper, we proposed a system which maintains the attendance records of students automatically. Manual entering of attendance in log books becomes a difficult task and it also wastes the time. Reading out the names of each student, each hour destroys the precious time. So we designed an efficient module that comprises of a fingerprint sensor to manage the attendance records of students. Our module enrolls the student's as well as staff's fingerprints. This enrolling is a onetime process and their fingerprints will be stored in the fingerprint sensor. During enrolling of fingerprints alone we require a system since it is a onetime process. You can have your own roll number as your fingerprint id which will be unique for each student and staff. After enrolling process gets completed you can disconnect the module from the system and insert a 9v battery into the module. This will provide power when the module is not connected with the system. Then the module can be taken to the class and the presence of students can be get. The presence of each students will be updated in a database and the data will be passed to the server using Wi-Fi. If a student is absent for a particular class automatically a SMS will be sent to their parents. If a student is absent continuously for more than three days a message intimating the parents to meet the HOD will be sent automatically. So everything here gets automated. Also a unique username and password for staff members are given in a website we create and the website can display the student's details, their attendance percentage which makes the work simple. Also mails and messages can be sent by the staff members using that site to intimate any urgent messages to the parents.

This paper is demonstrating to create a system of multifactor authentication based on biometric verification. Our system use iris for the first factor and fingerprint for the second factor. Once an attacker attempts to attack the system, there must have two factors. If one of them is compromised or broken, the attacker still has at least one more barrier to breach before successfully breaking into the target. Furthermore, this system will be implemented to enhance security for accessing control login government system.

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 ...

Biometric recognition refers to the use of biological characteristics for identification and verification of individuals. Use of biometric is increasing nowadays because biometric characteristics are difficult to replicate and lifelong. Applications which require only the authorized persons to access the resources are information systems, National id systems, voter and driver registrations, documentations, military area and ATMs. Among all biometrics, fingerprints is the most widely used and accepted by the user as the acquisition of fingerprint image is minimally invasive and require little hardware. Traditional fingerprint identification algorithm based on Minutiae Extraction, Discrete cosine transform(DCT) and fast Fourier transform require so much computation. Wavelet based algorithm do not require any preprocessing and post processing steps and hence is the key for low cost fingerprint identification system. This paper presents the overview of different fingerprint recognition system. In comparison to older fingerprint recognition system that is based on minutiae and FFT, the wavelet based system has high recognition rates.

The Fingerprint images are used to identify the person uniquely. The special system known as  Fingerprint  Recognition  System  (FRS)  is  used  to  match  Fingerprints.  The  overall  matching  and recognition  should  be  accurate,  so  that  it  can  be  used  in  the  restricted  areas.  It  is  very  important  to consider  the  performance  of  the  Fingerprint  Recognition  System  (FRS).  Today’s  world  has  a  common question i.e. ‘Why Fingerprints are not commonly used?’ The answer of this question is the inefficient FRS method. User identification and verification techniques are designed in such a way that only authorized users are allowed to access. So, it is require studying the functional approaches by analyzing the problems regarding verification. This research paper experimentally present the performance evaluation of Minutia Matching based FRS by using of false acceptance rate (FAR) and false rejection rate (FRR). The result of user attempts evaluates by calculating Hough Transformation.

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 and fingerprint images. Salient features of the face and fingerprint were extracted using the Gabor filter technique. A dimensionality reduction technique was carried out on both images extracted features using a principal component analysis technique. A feature level fusion algorithm (Mahalanobis distance technique) is used to combine each unimodal feature together. The performance of the proposed approach is validated and is effective.

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This article describes the development of DSP as the core of the face recognition system, on the basis of understanding the background, significance and current research situation at home and abroad of face recognition issue, having a in-depth study to face detection, Image preprocessing, feature extraction face facial structure, facial expression feature extraction, classification and other issues during face recognition and have achieved research and development of DSP-based face recognition system for robotic rehabilitation nursing beds. The system uses a fixed-point DSP TMS320DM642 as a central processing unit, with a strong processing performance, high flexibility and programmability.

The increased development of IT tools and social communication networks has significantly increased the need for people to be identified with reliable and secure tools hence the importance of using biometric technology. Biometrics is an emerging field where technology improves our ability to identify a person. The advantage of biometric identification is that each individual has its own physical characteristics that cannot be changed, lost or stolen. The use of fingerprinting is today one of the most reliable technologies on the market to authenticate an individual. This technology is simple to use and easy to implement. The techniques of fingerprint recognition are numerous and diversified, they are generally based on generic algorithms and tools for filtering images. This article proposes a fingerprint recognition chain by highlighting improved processing tools to facilitate data retrieval. The results are retrieved and validated using Matlab.

—This research studied the question: " Are all individual's performance stable in a fingerprint recognition system? " The fingerprints of 154 individuals, provided at different force levels, were examined using the biometric menagerie tool, first coined by Doddington et al. in 1998. The Biometric Menagerie illustrates how each person in a given dataset performs in a biometric system, by using their genuine and impostor scores, and providing them a classification based upon those scores. This research examined the biometric menagerie classifications across different force levels in a fingerprint recognition study to uncover if individuals performed the same over five force levels. The study concluded that they did not, and a new metric has been created to quantify this phenomenon. As a result of this discovery, the new metric, Stability Score Index is described to showcase the movement of individuals in the menagerie.