This thesis is dedicated to extracting expression robust features for 3D face recognition. The us... more This thesis is dedicated to extracting expression robust features for 3D face recognition. The use of 3D imaging enables the extraction of discriminative features that can significantly improve the recognition performance due to the availability of facial surface information such as depth, surface normals and curvature. Expression robust analysis using information from both depth and surface normals is investigated by dividing the main facial region into patches of different scales. The nasal region and adjoining parts of the cheeks are utilized as they are more consistent over different expressions and are hard to deliberately occlude. In addition, in comparison with other parts of the face, these regions have a high potential to produce discriminative features for recognition and overcome pose variations. An overview and classification methodology of the widely used 3D face databases are first introduced to provide an appropriate reference for 3D face database selection. Using the...
In recent decades, many 3D data acquisition methods have been developed to provide accurate and c... more In recent decades, many 3D data acquisition methods have been developed to provide accurate and cost-effective 3D captures of the human face. An example system, which can accommodate both research and commercial applications, is the Photoface device. Photoface is based on the photometric stereo imaging technique. To improve the recognition performance using Photoface captures, a novel landmarking algorithm is first proposed by thresholding surface normals maps. The development of landmarking algorithms specifically for photometric stereo captures enables region-based feature extraction and fills a gap in the 3D face landmarking literature. Nasal curves and spherical patches are then used respectively for recognition and are evaluated on the 3DE-VISIR database, which contains Photoface captures with expressions. The neutral vs. non-neutral matching results demonstrate high face recognition performance using spherical patches and a KFA classifier, achieving a R1RR of 97.26% when only ...
Cancer is one of the most common causes of death in humans. It can arise from many different cell... more Cancer is one of the most common causes of death in humans. It can arise from many different cell types, and even cancers originating from the same tissue can constitute a heterogeneous group of di ...
3D face recognition is an increasing popular modality for biometric authentication, for example i... more 3D face recognition is an increasing popular modality for biometric authentication, for example in the iPhoneX. Landmarking plays a significant role in region based face recognition algorithms. The accuracy and consistency of the landmarking will directly determine the effectiveness of feature extraction and hence the overall recognition performance. While surface normals have been shown to provide high performing features for face recognition, their use in landmarking has not been widely explored. To this end, a new 3D facial landmarking algorithm based on thresholded surface normals maps is proposed, which is applicable to widely used 3D face databases. The benefits of employing surface normals are demonstrated for both facial roll and yaw rotation calibration and nasal landmarks localisation. Results on the Bosphorus, FRGC and BU-3DFE databases show that the detected landmarks possess high within-class consistency and accuracy under different expressions. For several key landmarks the performance achieved surpasses that of state-of-the-art techniques and is also training free and computationally efficient. The use of surface normals therefore provides a useful representation of the 3D surface and the proposed landmarking algorithm provides an effective approach to localising the key nasal landmarks.
This paper proposes an algorithm is proposed to quantitatively evaluate the performance of 3D hol... more This paper proposes an algorithm is proposed to quantitatively evaluate the performance of 3D holistic face recognition algorithms when various denoising methods are used. First, a method is proposed to model the noise on the 3D face datasets. The model not only identifies those regions on the face which are sensitive to the noise but can also be used to simulate noise for any given 3D face. Then, by incorporating the noise model in a novel 3D face recognition pipeline, seven different classification and matching methods and six denoising techniques are used to quantify the face recognition algorithms performance for different powers of the noise. The outcome: (1) shows the most reliable parameters for the denoising methods to be used in a 3D face recognition pipeline; (2) shows which parts of the face are more vulnerable to noise and require further post-processing after data acquisition; (3) compares the performance of three different categories of recognition algorithms: training-free matching-based, subspace projection-based and training-based (without projection) classifiers. The results show the high performance of the bootstrap aggregating tree classifiers and median filtering for very high intensity noise. Also, when different noisy/denoised samples are used as probes or in the gallery, the matching algorithms significantly outperform the training-based (including the subspace projection) methods.
Background: Studies that aim at explaining phenotypes or disease susceptibility by genetic or epi... more Background: Studies that aim at explaining phenotypes or disease susceptibility by genetic or epigenetic variants often rely on clustering methods to stratify individuals or samples. While statistical associations may point at increased risk for certain parts of the population, the ultimate goal is to make precise predictions for each individual. This necessitates tools that allow for the rapid inspection of each data point, in particular to find explanations for outliers. Results: ACES is an integrative cluster-and phenotype-browser, which implements standard clustering methods, as well as multiple visualization methods in which all sample information can be displayed quickly. In addition, ACES can automatically mine a list of phenotypes for cluster enrichment, whereby the number of clusters and their boundaries are estimated by a novel method. For visual data browsing, ACES provides a 2D or 3D PCA or Heat Map view. ACES is implemented in Java, with a focus on a user-friendly, interactive, graphical interface. Conclusions: ACES has been proven an invaluable tool for analyzing large, pre-filtered DNA methylation data sets and RNA-Sequencing data, due to its ease to link molecular markers to complex phenotypes.
This paper proposes a novel local depth and surface normals descriptor to explore the discriminat... more This paper proposes a novel local depth and surface normals descriptor to explore the discriminative features on the nasal surface and the adjoining cheek regions for expression robust 3D face recognition. After preprocessing the 3D face data, landmarks located on the perimeter of a triangular region covering the nose and adjoining parts of the cheeks are accurately detected. Inspired by Local Binary Patterns, local shape differences for 3D points on a set of horizontal curves joining selected landmarks provide a novel representation of the local shape information. A further analysis of the discriminatory power of each patch shows that the adjoining regions have the potential to produce good recognition performance. Using the FRGC and Bosphorus databases, the performance of the proposed descriptor is evaluated on diverse patches, scales and for four components, one from the depth and three from the surface normals. Results show that the new local shape descriptor performs well at representing the shape information on a relatively large scale. On the basis of this descriptor, a relatively small set of features extracted from the nasal and adjoining cheek regions produce a R1RR of 97.76% and an EER of 1.32%. The adjoining cheek regions demonstrate a high discriminatory power and provide a useful new addition to 3D face biometrics.
2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015
This paper proposes a novel local depth and surface normals descriptor to explore the discriminat... more This paper proposes a novel local depth and surface normals descriptor to explore the discriminative features on the nasal surface and the adjoining cheek regions for expression robust 3D face recognition. After preprocessing the 3D face data, landmarks located on the perimeter of a triangular region covering the nose and adjoining parts of the cheeks are accurately detected. Inspired by Local Binary Patterns, local shape differences for 3D points on a set of horizontal curves joining selected landmarks provide a novel representation of the local shape information. A further analysis of the discriminatory power of each patch shows that the adjoining regions have the potential to produce good recognition performance. Using the FRGC and Bosphorus databases, the performance of the proposed descriptor is evaluated on diverse patches, scales and for four components, one from the depth and three from the surface normals. Results show that the new local shape descriptor performs well at representing the shape information on a relatively large scale. On the basis of this descriptor, a relatively small set of features extracted from the nasal and adjoining cheek regions produce a R1RR of 97.76% and an EER of 1.32%. The adjoining cheek regions demonstrate a high discriminatory power and provide a useful new addition to 3D face biometrics.
This study proposes an algorithm is proposed to quantitatively evaluate the performance of three-... more This study proposes an algorithm is proposed to quantitatively evaluate the performance of three-dimensional (3D) holistic face recognition algorithms when various denoising methods are used. First, a method is proposed to model the noise on the 3D face datasets. The model not only identifies those regions on the face which are sensitive to the noise but can also be used to simulate noise for any given 3D face. Then, by incorporating the noise model in a novel 3D face recognition pipeline, seven different classification and matching methods and six denoising techniques are used to quantify the face recognition algorithms performance for different powers of the noise. The outcome: (i) shows the most reliable parameters for the denoising methods to be used in a 3D face recognition pipeline; (ii) shows which parts of the face are more vulnerable to noise and require further post-processing after data acquisition; and (iii) compares the performance of three different categories of recognition algorithms: training-free matching-based, subspace projection-based and training-based (without projection) classifiers. The results show the high performance of the bootstrap aggregating tree classifiers and median filtering for very high intensity noise. Moreover, when different noisy/denoised samples are used as probes or in the gallery, the matching algorithms significantly outperform the training-based (including the subspace projection) methods.
In the past decade, expression variations have been one of the most challenging sources of variab... more In the past decade, expression variations have been one of the most challenging sources of variability in 3D face recognition, especially for scenarios where there are a large number of face samples to discriminate between. In this paper, an expression robust rejector is proposed that first robustly locates landmarks on the relatively stable structure of the nose and its environs, termed the cheek/nose region. Then, by defining curves connecting the landmarks, a small set of features (4 curves with only 15 points each) on the cheek/nose surface are selected using the Bosphorus database. The resulting rejector, which can quickly eliminate a large number of candidates at an early stage, is further evaluated on the FRGC database for both the identification and verification scenarios. The classification performance using only 60 points from 4 curves shows the effectiveness of this efficient expression robust rejector.
In this paper, a new face detection algorithm based on fractal theory and complexion model is pro... more In this paper, a new face detection algorithm based on fractal theory and complexion model is proposed. On the basis of the traditional face detection, the new algorithm estimated the fractal dimension of face area to exclude the nonface area and extract the face area fast and accurately. Furthermore, its calculation is simple and it can realize multiangle detection in the complex background. As well as the new algorithm can overcome the shortcomings of high misdetection and low capability in adapting to changes of traditional way. Simulations are carried out with detailed analyses, which demonstrating the superior accuracy and high efficiency of our algorithm.
In this paper, we propose a new fractal-based face detection scheme in video stream by employing ... more In this paper, we propose a new fractal-based face detection scheme in video stream by employing some related properties of video decoding in MPEG-4. According to the types of video frames, two different solutions are applied to face detection. Faces are extracted by estimating the fractal dimension of face areas and considering previous Groups of VOP (GOV) to exclude the non-face areas.
This thesis is dedicated to extracting expression robust features for 3D face recognition. The us... more This thesis is dedicated to extracting expression robust features for 3D face recognition. The use of 3D imaging enables the extraction of discriminative features that can significantly improve the recognition performance due to the availability of facial surface information such as depth, surface normals and curvature. Expression robust analysis using information from both depth and surface normals is investigated by dividing the main facial region into patches of different scales. The nasal region and adjoining parts of the cheeks are utilized as they are more consistent over different expressions and are hard to deliberately occlude. In addition, in comparison with other parts of the face, these regions have a high potential to produce discriminative features for recognition and overcome pose variations. An overview and classification methodology of the widely used 3D face databases are first introduced to provide an appropriate reference for 3D face database selection. Using the...
In recent decades, many 3D data acquisition methods have been developed to provide accurate and c... more In recent decades, many 3D data acquisition methods have been developed to provide accurate and cost-effective 3D captures of the human face. An example system, which can accommodate both research and commercial applications, is the Photoface device. Photoface is based on the photometric stereo imaging technique. To improve the recognition performance using Photoface captures, a novel landmarking algorithm is first proposed by thresholding surface normals maps. The development of landmarking algorithms specifically for photometric stereo captures enables region-based feature extraction and fills a gap in the 3D face landmarking literature. Nasal curves and spherical patches are then used respectively for recognition and are evaluated on the 3DE-VISIR database, which contains Photoface captures with expressions. The neutral vs. non-neutral matching results demonstrate high face recognition performance using spherical patches and a KFA classifier, achieving a R1RR of 97.26% when only ...
Cancer is one of the most common causes of death in humans. It can arise from many different cell... more Cancer is one of the most common causes of death in humans. It can arise from many different cell types, and even cancers originating from the same tissue can constitute a heterogeneous group of di ...
3D face recognition is an increasing popular modality for biometric authentication, for example i... more 3D face recognition is an increasing popular modality for biometric authentication, for example in the iPhoneX. Landmarking plays a significant role in region based face recognition algorithms. The accuracy and consistency of the landmarking will directly determine the effectiveness of feature extraction and hence the overall recognition performance. While surface normals have been shown to provide high performing features for face recognition, their use in landmarking has not been widely explored. To this end, a new 3D facial landmarking algorithm based on thresholded surface normals maps is proposed, which is applicable to widely used 3D face databases. The benefits of employing surface normals are demonstrated for both facial roll and yaw rotation calibration and nasal landmarks localisation. Results on the Bosphorus, FRGC and BU-3DFE databases show that the detected landmarks possess high within-class consistency and accuracy under different expressions. For several key landmarks the performance achieved surpasses that of state-of-the-art techniques and is also training free and computationally efficient. The use of surface normals therefore provides a useful representation of the 3D surface and the proposed landmarking algorithm provides an effective approach to localising the key nasal landmarks.
This paper proposes an algorithm is proposed to quantitatively evaluate the performance of 3D hol... more This paper proposes an algorithm is proposed to quantitatively evaluate the performance of 3D holistic face recognition algorithms when various denoising methods are used. First, a method is proposed to model the noise on the 3D face datasets. The model not only identifies those regions on the face which are sensitive to the noise but can also be used to simulate noise for any given 3D face. Then, by incorporating the noise model in a novel 3D face recognition pipeline, seven different classification and matching methods and six denoising techniques are used to quantify the face recognition algorithms performance for different powers of the noise. The outcome: (1) shows the most reliable parameters for the denoising methods to be used in a 3D face recognition pipeline; (2) shows which parts of the face are more vulnerable to noise and require further post-processing after data acquisition; (3) compares the performance of three different categories of recognition algorithms: training-free matching-based, subspace projection-based and training-based (without projection) classifiers. The results show the high performance of the bootstrap aggregating tree classifiers and median filtering for very high intensity noise. Also, when different noisy/denoised samples are used as probes or in the gallery, the matching algorithms significantly outperform the training-based (including the subspace projection) methods.
Background: Studies that aim at explaining phenotypes or disease susceptibility by genetic or epi... more Background: Studies that aim at explaining phenotypes or disease susceptibility by genetic or epigenetic variants often rely on clustering methods to stratify individuals or samples. While statistical associations may point at increased risk for certain parts of the population, the ultimate goal is to make precise predictions for each individual. This necessitates tools that allow for the rapid inspection of each data point, in particular to find explanations for outliers. Results: ACES is an integrative cluster-and phenotype-browser, which implements standard clustering methods, as well as multiple visualization methods in which all sample information can be displayed quickly. In addition, ACES can automatically mine a list of phenotypes for cluster enrichment, whereby the number of clusters and their boundaries are estimated by a novel method. For visual data browsing, ACES provides a 2D or 3D PCA or Heat Map view. ACES is implemented in Java, with a focus on a user-friendly, interactive, graphical interface. Conclusions: ACES has been proven an invaluable tool for analyzing large, pre-filtered DNA methylation data sets and RNA-Sequencing data, due to its ease to link molecular markers to complex phenotypes.
This paper proposes a novel local depth and surface normals descriptor to explore the discriminat... more This paper proposes a novel local depth and surface normals descriptor to explore the discriminative features on the nasal surface and the adjoining cheek regions for expression robust 3D face recognition. After preprocessing the 3D face data, landmarks located on the perimeter of a triangular region covering the nose and adjoining parts of the cheeks are accurately detected. Inspired by Local Binary Patterns, local shape differences for 3D points on a set of horizontal curves joining selected landmarks provide a novel representation of the local shape information. A further analysis of the discriminatory power of each patch shows that the adjoining regions have the potential to produce good recognition performance. Using the FRGC and Bosphorus databases, the performance of the proposed descriptor is evaluated on diverse patches, scales and for four components, one from the depth and three from the surface normals. Results show that the new local shape descriptor performs well at representing the shape information on a relatively large scale. On the basis of this descriptor, a relatively small set of features extracted from the nasal and adjoining cheek regions produce a R1RR of 97.76% and an EER of 1.32%. The adjoining cheek regions demonstrate a high discriminatory power and provide a useful new addition to 3D face biometrics.
2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015
This paper proposes a novel local depth and surface normals descriptor to explore the discriminat... more This paper proposes a novel local depth and surface normals descriptor to explore the discriminative features on the nasal surface and the adjoining cheek regions for expression robust 3D face recognition. After preprocessing the 3D face data, landmarks located on the perimeter of a triangular region covering the nose and adjoining parts of the cheeks are accurately detected. Inspired by Local Binary Patterns, local shape differences for 3D points on a set of horizontal curves joining selected landmarks provide a novel representation of the local shape information. A further analysis of the discriminatory power of each patch shows that the adjoining regions have the potential to produce good recognition performance. Using the FRGC and Bosphorus databases, the performance of the proposed descriptor is evaluated on diverse patches, scales and for four components, one from the depth and three from the surface normals. Results show that the new local shape descriptor performs well at representing the shape information on a relatively large scale. On the basis of this descriptor, a relatively small set of features extracted from the nasal and adjoining cheek regions produce a R1RR of 97.76% and an EER of 1.32%. The adjoining cheek regions demonstrate a high discriminatory power and provide a useful new addition to 3D face biometrics.
This study proposes an algorithm is proposed to quantitatively evaluate the performance of three-... more This study proposes an algorithm is proposed to quantitatively evaluate the performance of three-dimensional (3D) holistic face recognition algorithms when various denoising methods are used. First, a method is proposed to model the noise on the 3D face datasets. The model not only identifies those regions on the face which are sensitive to the noise but can also be used to simulate noise for any given 3D face. Then, by incorporating the noise model in a novel 3D face recognition pipeline, seven different classification and matching methods and six denoising techniques are used to quantify the face recognition algorithms performance for different powers of the noise. The outcome: (i) shows the most reliable parameters for the denoising methods to be used in a 3D face recognition pipeline; (ii) shows which parts of the face are more vulnerable to noise and require further post-processing after data acquisition; and (iii) compares the performance of three different categories of recognition algorithms: training-free matching-based, subspace projection-based and training-based (without projection) classifiers. The results show the high performance of the bootstrap aggregating tree classifiers and median filtering for very high intensity noise. Moreover, when different noisy/denoised samples are used as probes or in the gallery, the matching algorithms significantly outperform the training-based (including the subspace projection) methods.
In the past decade, expression variations have been one of the most challenging sources of variab... more In the past decade, expression variations have been one of the most challenging sources of variability in 3D face recognition, especially for scenarios where there are a large number of face samples to discriminate between. In this paper, an expression robust rejector is proposed that first robustly locates landmarks on the relatively stable structure of the nose and its environs, termed the cheek/nose region. Then, by defining curves connecting the landmarks, a small set of features (4 curves with only 15 points each) on the cheek/nose surface are selected using the Bosphorus database. The resulting rejector, which can quickly eliminate a large number of candidates at an early stage, is further evaluated on the FRGC database for both the identification and verification scenarios. The classification performance using only 60 points from 4 curves shows the effectiveness of this efficient expression robust rejector.
In this paper, a new face detection algorithm based on fractal theory and complexion model is pro... more In this paper, a new face detection algorithm based on fractal theory and complexion model is proposed. On the basis of the traditional face detection, the new algorithm estimated the fractal dimension of face area to exclude the nonface area and extract the face area fast and accurately. Furthermore, its calculation is simple and it can realize multiangle detection in the complex background. As well as the new algorithm can overcome the shortcomings of high misdetection and low capability in adapting to changes of traditional way. Simulations are carried out with detailed analyses, which demonstrating the superior accuracy and high efficiency of our algorithm.
In this paper, we propose a new fractal-based face detection scheme in video stream by employing ... more In this paper, we propose a new fractal-based face detection scheme in video stream by employing some related properties of video decoding in MPEG-4. According to the types of video frames, two different solutions are applied to face detection. Faces are extracted by estimating the fractal dimension of face areas and considering previous Groups of VOP (GOV) to exclude the non-face areas.
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Papers by Jiangning Gao