The Experts below are selected from a list of 282 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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Learning deep compact similarity metric for Kinship verification from face images
Information Fusion, 2019Co-Authors: Xiuzhuang Zhou, Min XuAbstract:Abstract Recent advances in Kinship verification have shown that learning an appropriate Kinship similarity metric on human faces plays a critical role in this problem. However, most of existing distance metric learning (DML) based solutions rely on linearity assumption of the Kinship metric model, and the domain knowledge of large cross-generation discrepancy (e.g., large age span and gender difference between parent and child images) has not been considered in metric learning, leading to degraded performance for genetic similarity measure on human faces. To address these limitations, we propose in this work a new Kinship metric learning (KML) method with a coupled deep neural network (DNN) model. KML explicitly models the cross-generation discrepancy inherent on parent-child pairs, and learns a coupled deep similarity metric such that the image pairs with Kinship Relation are pulled close, while those without Kinship Relation (but with high appearance similarity) are pushed as far away as possible. Moreover, by imposing the intra-connection diversity and inter-connection consistency over the coupled DNN, we introduce the property of hierarchical compactness into the coupled network to facilitate deep metric learning with limited amount of Kinship training data. Empirically, we evaluate our algorithm on several Kinship benchmarks against the state-of-the-art DML alternatives, and the results demonstrate the superiority of our method.
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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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CVPR - Neighborhood repulsed metric learning for Kinship verification
IEEE transactions on pattern analysis and machine intelligence, 2012Co-Authors: Jiwen Lu, Xiuzhuang Zhou, Yuanyuan Shang, Junlin Hu, Gang WangAbstract: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.
Jiwen Lu - One of the best experts on this subject based on the ideXlab platform.
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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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CVPR - Neighborhood repulsed metric learning for Kinship verification
IEEE transactions on pattern analysis and machine intelligence, 2012Co-Authors: Jiwen Lu, Xiuzhuang Zhou, Yuanyuan Shang, Junlin Hu, Gang WangAbstract: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.
Jie Zhou - One of the best experts on this subject based on the ideXlab platform.
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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.
Yuanyuan Shang - One of the best experts on this subject based on the ideXlab platform.
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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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CVPR - Neighborhood repulsed metric learning for Kinship verification
IEEE transactions on pattern analysis and machine intelligence, 2012Co-Authors: Jiwen Lu, Xiuzhuang Zhou, Yuanyuan Shang, Junlin Hu, Gang WangAbstract: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.
Chuangyin Dang - One of the best experts on this subject based on the ideXlab platform.
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Weighted Graph Embedding-Based Metric Learning for Kinship Verification
IEEE Transactions on Image Processing, 2019Co-Authors: Jianqing Liang, Qinghua Hu, Chuangyin DangAbstract:Given a group photograph, it is interesting and useful to judge whether the characters in it share specific Kinship Relation, such as father-daughter, father-son, mother-daughter, or mother-son. Recently, facial image-based Kinship verification has attracted wide attention in computer vision. Some metric learning algorithms have been developed for improving Kinship verification. However, most of the existing algorithms ignore fusing multiple feature representations and utilizing kernel techniques. In this paper, we develop a novel weighted graph embedding-based metric learning (WGEML) framework for Kinship verification. Inspired by the fact that family members usually show high similarity in facial features like eyes, noses, and mouths, despite their diversity, we jointly learn multiple metrics by constructing an intrinsic graph and two penalty graphs to characterize the intraclass compactness and interclass separability for each feature representation, respectively, so that both the consistency and complementarity among multiple features can be fully exploited. Meanwhile, combination weights are determined through a weighted graph embedding framework. Furthermore, we present a kernelized version of WGEML to tackle nonlinear problems. Experimental results demonstrate both the effectiveness and efficiency of our proposed methods.
