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Journal ArticleDOI

Hierarchical Representation Learning for Kinship Verification

01 Jan 2017-IEEE Transactions on Image Processing (IEEE Trans Image Process)-Vol. 26, Iss: 1, pp 289-302
TL;DR: In this paper, a hierarchical kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner, and a compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is employed to verify the kin accurately.
Abstract: 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.
Citations
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Journal ArticleDOI
TL;DR: A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is presented in this paper, where the authors provide a comprehensive overview of the role of information fusion in biometrics.

151 citations

Journal ArticleDOI
TL;DR: This work proposes a new kinship metric learning (KML) method with a coupled deep neural network (DNN) model, and introduces the property of hierarchical compactness into the coupled network to facilitate deep metric learning with limited amount of kinship training data.

59 citations

Journal ArticleDOI
TL;DR: A novel weighted graph embedding-based metric learning (WGEML) framework for kinship verification based on the fact that family members usually show high similarity in facial features, despite their diversity is developed.
Abstract: 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.

55 citations

Posted Content
TL;DR: The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics with specific focus on three questions: what to fusion, when to fuse, and how to fuse.
Abstract: The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of single-biometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.

47 citations

Proceedings ArticleDOI
27 Oct 2017
TL;DR: In this work, KinNet is proposed, a fine-to-coarse deep metric learning framework for kinship verification, which transfers knowledge from the large-scale-data-driven face recognition task by pre-training the network with massive data for face recognition.
Abstract: Automatic kinship verification has attracted increasing attentions as it holds promise to an abundance of applications. However, existing kinship verification methods suffer from the lack of large scale real-world data. Without enough training data, it is difficult to learn proper features that are discriminant for blood-related peoples. In this work, we propose KinNet, a fine-to-coarse deep metric learning framework for kinship verification. In the framework, we transfer knowledge from the large-scale-data-driven face recognition task, which is a fine-grained version of kinship recognition, by pre-training the network with massive data for face recognition. Then, the network is fine-tuned to find a metric space where kin-related peoples are discriminant. The metric space is learned by minimizing a soft triplet loss on the augmented kinship dataset. An augmented strategy is proposed to balance the amount of images per family member. Finally, we ensemble four networks to further boost the performance. The experimental results on the 1st Large-Scale Kinship Recognition Data Challenge (Track 1) demonstrate that our KinNet achieves the state-of-the-art performance in kinship verification.

39 citations

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01 Jan 1998
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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TL;DR: In this paper, the basic theory of Maximum Likelihood Estimation (MLE) is used to detect a difference between two different proportions of a given proportion in a single proportion.
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16,435 citations

Journal ArticleDOI
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Abstract: We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.

15,055 citations