Hierarchical Representation Learning for Kinship Verification
TLDR
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.read more
Citations
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Learning deep compact similarity metric for kinship verification from face images
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Weighted Graph Embedding-Based Metric Learning for Kinship Verification
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.
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References
More filters
Journal ArticleDOI
Face Description with Local Binary Patterns: Application to Face Recognition
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Proceedings ArticleDOI
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
TL;DR: The convolutional deep belief network is presented, a hierarchical generative model which scales to realistic image sizes and is translation-invariant and supports efficient bottom-up and top-down probabilistic inference.
Journal ArticleDOI
Statistical Methods for Rates and Proportions
Proceedings ArticleDOI
Face recognition in unconstrained videos with matched background similarity
Lior Wolf,Tal Hassner,Itay Maoz +2 more
TL;DR: A comprehensive database of labeled videos of faces in challenging, uncontrolled conditions, the ‘YouTube Faces’ database, along with benchmark, pair-matching tests are presented and a novel set-to-set similarity measure, the Matched Background Similarity (MBGS), is described.
Proceedings Article
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
TL;DR: It is found empirically that this penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold.
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