The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Xiuzhuang Zhou - One of the best experts on this subject based on the ideXlab platform.
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Prototype-Based Discriminative Feature Learning for Kinship Verification
IEEE transactions on cybernetics, 2014Co-Authors: Haibin Yan, Xiuzhuang ZhouAbstract:In this paper, we propose a new prototype-based discriminative feature learning (PDFL) method for Kinship verification. Unlike most previous Kinship verification methods which employ low-level hand-crafted descriptors such as local binary pattern and Gabor features for face representation, this paper aims to learn discriminative mid-level features to better characterize the kin relation of face images for Kinship verification. To achieve this, we construct a set of face samples with unlabeled kin relation from the labeled face in the wild dataset as the reference set. Then, each sample in the training face Kinship dataset is represented as a mid-level feature vector, where each entry is the corresponding decision value from one support vector machine hyperplane. Subsequently, we formulate an optimization function by minimizing the intraclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kin relation) with the mid-level features. To better use multiple low-level features for mid-level feature learning, we further propose a multiview PDFL method to learn multiple mid-level features to improve the verification performance. Experimental results on four publicly available Kinship datasets show the superior performance of the proposed methods over both the state-of-the-art Kinship verification methods and human ability in our Kinship verification task.
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Discriminative Multimetric Learning for Kinship Verification
IEEE Transactions on Information Forensics and Security, 2014Co-Authors: Jiwen Lu, Weihong Deng, Xiuzhuang ZhouAbstract:In this paper, we propose a new discriminative multimetric learning method for Kinship verification via facial image analysis. Given each face image, we first extract multiple features using different face descriptors to characterize face images from different aspects because different feature descriptors can provide complementary information. Then, we jointly learn multiple distance metrics with these extracted multiple features under which the probability of a pair of face image with a Kinship relation having a smaller distance than that of the pair without a Kinship relation is maximized, and the correlation of different features of the same face sample is maximized, simultaneously, so that complementary and discriminative information is exploited for verification. Experimental results on four face Kinship data sets show the effectiveness of our proposed method over the existing single-metric and multimetric learning methods.
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Neighborhood Repulsed Metric Learning for Kinship Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014Co-Authors: Jiwen Lu, Xiuzhuang Zhou, Yuanyuan Shang, Jie ZhouAbstract:Kinship verification from facial images is an interesting and challenging problem in computer vision, and there are very limited attempts on tackle this problem in the literature. In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for Kinship verification. Motivated by the fact that interclass samples (without a Kinship relation) with higher similarity usually lie in a neighborhood and are more easily misclassified than those with lower similarity, we aim to learn a distance metric under which the intraclass samples (with a Kinship relation) are pulled as close as possible and interclass samples lying in a neighborhood are repulsed and pushed away as far as possible, simultaneously, such that more discriminative information can be exploited for verification. To make better use of multiple feature descriptors to extract complementary information, we further propose a multiview NRML (MNRML) method to seek a common distance metric to perform multiple feature fusion to improve the Kinship verification performance. Experimental results are presented to demonstrate the efficacy of our proposed methods. Finally, we also test human ability in Kinship verification from facial images and our experimental results show that our methods are comparable to that of human observers.
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Kinship verification from facial images under uncontrolled conditions
ACM Multimedia, 2011Co-Authors: Xiuzhuang Zhou, Yuanyuan Shang, Yong GuanAbstract:In this paper, we present an automatic Kinship verification system based on facial image analysis under uncontrolled conditions. While a large number of studies on human face analysis have been performed in the literature, there are a few attempts on automatic face analysis for Kinship verification, possibly due to lacking of such publicly available databases and great challenges of this problem. To this end, we collect a Kinship face database by searching 400+ pairs of public figures and celebrities from the internet, and automatically detect them with the Viola-Jones face detector. Then, we propose a new spatial pyramid learning-based (SPLE) feature descriptor for face representation and apply support vector machine (SVM) for Kinship verification. The proposed system has the following three characteristics: 1) no manual human annotation of face landmarks is required and the Kinship information is automatically obtained from the original pair of images; 2) both local appearance information and global spatial information have been effectively utilized in the proposed SPLE feature descriptor, and better performance can be obtained than state-of-the-art feature descriptors in our application; 3) the performance of our proposed system is comparable to that of human observers.
Angshul Majumdar - One of the best experts on this subject based on the ideXlab platform.
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hierarchical representation learning for Kinship verification
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Naman Kohli, Mayank Vatsa, Richa Singh, Afzel Noore, Angshul MajumdarAbstract:Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this research, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate Kinship-cues. Utilizing the information obtained from the human study, a hierarchical Kinship Verification via Representation Learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU Kinship Database is created which consists of multiple images per subject to facilitate Kinship verification. The results show that the proposed deep learning framework (KVRL-fcDBN) yields stateof-the-art Kinship verification accuracy on the WVU Kinship database and on four existing benchmark datasets. Further, Kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification.
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hierarchical representation learning for Kinship verification
IEEE Transactions on Image Processing, 2017Co-Authors: Naman Kohli, Mayank Vatsa, Richa Singh, Afzel Noore, Angshul MajumdarAbstract:Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate Kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender and age and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index $d'$ , and perceptual information entropy. Utilizing the information obtained from the human study, a hierarchical Kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks ( fc DBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU Kinship database is created, which consists of multiple images per subject to facilitate Kinship verification. The results show that the proposed deep learning framework (KVRL- fc DBN) yields the state-of-the-art Kinship verification accuracy on the WVU Kinship database and on four existing benchmark data sets. Furthermore, Kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL- fc DBN framework, an improvement of over 20% is observed in the performance of face verification.
Haibin Yan - One of the best experts on this subject based on the ideXlab platform.
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video based Kinship verification using distance metric learning
Pattern Recognition, 2018Co-Authors: Haibin YanAbstract:Abstract In this paper, we investigate the problem of video-based Kinship verification via human face analysis. While several attempts have been made on facial Kinship verification from still images, to our knowledge, the problem of video-based Kinship verification has not been formally addressed in the literature. In this paper, we make the two contributions to video-based Kinship verification. On one hand, we present a new video face dataset called Kinship Face Videos in the Wild (KFVW) which were captured in wild conditions for the video-based Kinship verification study, as well as the standard benchmark. On the other hand, we employ our benchmark to evaluate and compare the performance of several state-of-the-art metric learning based Kinship verification methods. Experimental results are presented to demonstrate the efficacy of our proposed dataset and the effectiveness of existing metric learning methods for video-based Kinship verification. Lastly, we also evaluate human ability on Kinship verification from facial videos and experimental results show that metric learning based computational methods are not as good as that of human observers.
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Prototype-Based Discriminative Feature Learning for Kinship Verification
IEEE transactions on cybernetics, 2014Co-Authors: Haibin Yan, Xiuzhuang ZhouAbstract:In this paper, we propose a new prototype-based discriminative feature learning (PDFL) method for Kinship verification. Unlike most previous Kinship verification methods which employ low-level hand-crafted descriptors such as local binary pattern and Gabor features for face representation, this paper aims to learn discriminative mid-level features to better characterize the kin relation of face images for Kinship verification. To achieve this, we construct a set of face samples with unlabeled kin relation from the labeled face in the wild dataset as the reference set. Then, each sample in the training face Kinship dataset is represented as a mid-level feature vector, where each entry is the corresponding decision value from one support vector machine hyperplane. Subsequently, we formulate an optimization function by minimizing the intraclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kin relation) with the mid-level features. To better use multiple low-level features for mid-level feature learning, we further propose a multiview PDFL method to learn multiple mid-level features to improve the verification performance. Experimental results on four publicly available Kinship datasets show the superior performance of the proposed methods over both the state-of-the-art Kinship verification methods and human ability in our Kinship verification task.
Naman Kohli - One of the best experts on this subject based on the ideXlab platform.
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hierarchical representation learning for Kinship verification
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Naman Kohli, Mayank Vatsa, Richa Singh, Afzel Noore, Angshul MajumdarAbstract:Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this research, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate Kinship-cues. Utilizing the information obtained from the human study, a hierarchical Kinship Verification via Representation Learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU Kinship Database is created which consists of multiple images per subject to facilitate Kinship verification. The results show that the proposed deep learning framework (KVRL-fcDBN) yields stateof-the-art Kinship verification accuracy on the WVU Kinship database and on four existing benchmark datasets. Further, Kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification.
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hierarchical representation learning for Kinship verification
IEEE Transactions on Image Processing, 2017Co-Authors: Naman Kohli, Mayank Vatsa, Richa Singh, Afzel Noore, Angshul MajumdarAbstract:Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate Kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender and age and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index $d'$ , and perceptual information entropy. Utilizing the information obtained from the human study, a hierarchical Kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks ( fc DBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU Kinship database is created, which consists of multiple images per subject to facilitate Kinship verification. The results show that the proposed deep learning framework (KVRL- fc DBN) yields the state-of-the-art Kinship verification accuracy on the WVU Kinship database and on four existing benchmark data sets. Furthermore, Kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL- fc DBN framework, an improvement of over 20% is observed in the performance of face verification.
Maria Wilhelmus - One of the best experts on this subject based on the ideXlab platform.
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mediation in Kinship care another step in the provision of culturally relevant child welfare services
Social Work, 1998Co-Authors: Maria WilhelmusAbstract:Many pressing issues face the U.S. child welfare system. The system is being inundated with children in need of homes at a time when traditional foster placements are difficult to find (National Commission on Family Foster Care, 1991). An estimated 3,100,000 children were reported to child protective services agencies as alleged victims of child maltreatment in 1995 alone, with 996,000 of this number eventually confirmed (based on data collected from 37 states and the District of Columbia) (Daro, 1996). Also, child maltreatment reports are increasing (Pear, 1996). Between 1994 and 1995, this increase was an average of 2 percent per state, with the total number of reports nationwide showing an increase of 49 percent since 1986 (Daro, 1996). Not surprisingly, there has been a corresponding increase in the number of children entering foster care. From 1982 to 1992 there was a 62 percent increase in the number of children in out-of-home placements (U.S. Department of Health and Human Services, 1996). Finding homes for all the children in need of placement has become more difficult; the number of qualified foster homes has decreased 30 percent since 1984 (National Commission on Family Foster Care, 1991). Children of color are disproportionately represented in the foster care population (Davis, 1995; Hill, 1987), and there have been calls to increase "sensitivity to cultural context" in the delivery of child welfare services (Pinderhughes, 1991, p. 604). Kinship Care To meet the challenges posed by the large numbers of children and limited nonrelative foster care placements, the child welfare system has begun to actively incorporate Kinship care into its array of services (Berrick, Barth, N Gleeson, 1995). Although it is challenging to ascertain precise figures on a national level, in many jurisdictions it has been estimated that close to 50 percent of children removed from parental custody have been placed with relatives (Child Welfare League of America, 1994). Kinship care has been defined as "the full-time nurturing and protection of children who must be separated from their parents by relatives, members of their tribes or clans, godparents, step-parents, or other adults who have a Kinship bond with a child" (Child Welfare League of America, 1994, p. 2). Kinship care has been further defined as being provided by either "Kinship caregivers," who provide care that is not formally recognized by the foster care system, or "Kinship foster parents," who are recognized by the foster care system (Berrick et al., 1994). This dual terminology used in Kinship care captures the juxtaposition of a centuries-old tradition of extended family and community responsibility for the care of children with the modern child welfare system. Years before the existence of "Kinship foster parents," there were "Kinship caregivers" (Hill, 1987; National Commission on Family Foster Care, 1991; Stack, 1974). In a discussion of Kinship care, Scannapieco and Jackson (1996) reviewed the literature detailing the historical response of African American families to separation and loss. From West African tradition to the pre-Civil War era, through Reconstruction and up to the modern day, the shared commitment to children, family, and community - as evidenced in the emphasis on Kinship relationships - has been a great source of strength for African American families in the face of adversity. A modern example of the strength of African American Kinship ties is evidenced in the current practice of African American midlife and older women stepping in to raise children left parentless by the crack cocaine epidemic (Minkler & Roe, 1993). Even after recognition by the child welfare system, today's Kinship foster families closely resemble the more informal Kinship care relationships evidenced throughout history. Children in Kinship foster care are predominantly African American (Berrick et al., 1994; National Commission on Family Foster Care, 1991; Thornton, 1991). …
