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Proceedings ArticleDOI

Self-similarity representation of Weber faces for kinship classification

06 Dec 2012-pp 245-250

TL;DR: A kinship classification algorithm that uses the local description of the pre-processed Weber face image to outperforms an existing algorithm and yields a classification accuracy of 75.2%.

AbstractEstablishing kinship using images can be utilized as context information in different applications including face recognition. However, the process of automatically detecting kinship in facial images is a challenging and relatively less explored task. The reason for this includes limited availability of datasets as well as the inherent variations amongst kins. This paper presents a kinship classification algorithm that uses the local description of the pre-processed Weber face image. A kinship database is also prepared that contains images pertaining to 272 kin pairs. The database includes images of celebrities (and their kins) and has four ethnicity groups and seven kinship groups. The proposed algorithm outperforms an existing algorithm and yields a classification accuracy of 75.2%.

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Citations
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Journal ArticleDOI
TL;DR: A discriminative deep multi-metric learning method to jointly learn multiple neural networks, under which the correlation of different features of each sample is maximized, and the distance of each positive pair is reduced and that of each negative pair is enlarged.
Abstract: This paper presents a new discriminative deep metric learning (DDML) method for face and kinship verification in wild conditions. While metric learning has achieved reasonably good performance in face and kinship verification, most existing metric learning methods aim to learn a single Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, which cannot capture the nonlinear manifold where face images usually lie on. To address this, we propose a DDML method to train a deep neural network to learn a set of hierarchical nonlinear transformations to project face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged. To better use the commonality of multiple feature descriptors to make all the features more robust for face and kinship verification, we develop a discriminative deep multi-metric learning method to jointly learn multiple neural networks, under which the correlation of different features of each sample is maximized, and the distance of each positive pair is reduced and that of each negative pair is enlarged. Extensive experimental results show that our proposed methods achieve the acceptable results in both face and kinship verification.

209 citations


Cites methods from "Self-similarity representation of W..."

  • ...Representative methods in this category include skin color [3], histogram of gradient [3], [22], [24], Gabor wavelet [23], [24], gradient orientation pyramid [23], salient part [27], self-similarity [28], and dynamic spatio-temporal descriptor [25]....

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Journal ArticleDOI
TL;DR: Experimental results show the effectiveness of the proposed discriminative multimetric learning method for kinship verification via facial image analysis over the existing single-metric and multimetricLearning methods.
Abstract: 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.

177 citations


Cites background from "Self-similarity representation of W..."

  • ...In this section, we briefly review two related topics: 1) kinship verification, and 2) metric learning....

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Journal ArticleDOI
TL;DR: Experimental results on four publicly available kinship datasets show the superior performance of the proposed PDFL methods over both the state-of-the-art kinship verification methods and human ability in the kinships verification task.
Abstract: 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.

148 citations


Cites background or methods from "Self-similarity representation of W..."

  • ...Unlike most previous kinship verification works where low-level hand-crafted feature descriptors [15], [16], [18], [26], [42], [45], [49], [50], [52], [56], [57] such as local binary pattern (LBP) [1], [9] and Gabor features [29], [57] are employed for face representation, we learn discriminative mid-level features to better characterize the relation of face images for kinship verification....

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  • ...Existing feature representation methods include skin color [16], histogram of gradient [16], [45], [56], Gabor wavelet [13], [45], [50], [57], gradient orientation pyramid [57], LBP [8], [42], scaleinvariant feature transform (SIFT) [42], [45], [52], salient part [18], [49], self-similarity [26], and dynamic features combined with spatio-temporal appearance descriptor [11]....

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  • ...While the past five years have witnessed encouraging progress in this area [15], [16], [18], [26], [42], [45], [49], [50], [52], [56], [57], the problem of kinship verification still remains unsolved because it is extremely challenging to extract kin-related features from human ages, especially when face images are captured in the wild....

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Proceedings ArticleDOI
01 Dec 2013
TL;DR: By using features that describe facial dynamics and spatio-temporal appearance over smile expressions, it is shown that it is possible to improve the state of the art in this problem, and it is indeed possible to recognize kinship by resemblance of facial expressions.
Abstract: Kinship verification from facial appearance is a difficult problem. This paper explores the possibility of employing facial expression dynamics in this problem. By using features that describe facial dynamics and spatio-temporal appearance over smile expressions, we show that it is possible to improve the state of the art in this problem, and verify that it is indeed possible to recognize kinship by resemblance of facial expressions. The proposed method is tested on different kin relationships. On the average, 72.89% verification accuracy is achieved on spontaneous smiles.

100 citations


Cites methods or result from "Self-similarity representation of W..."

  • ...Similar to the results reported in [11] linear SVM is found to perform better than polynomial and RBF alternatives in our experiments....

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  • ...In [11], the Self Similarity Representation of Weber face (SSRW) algorithm is proposed....

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Proceedings ArticleDOI
04 May 2015
TL;DR: Most of the participants tackled the image-restricted challenge and experimental results demonstrated better kinship verification performance than the baseline methods provided by the organizers.
Abstract: The aim of the Kinship Verification in the Wild Evaluation (held in conjunction with the 2015 IEEE International Conference on Automatic Face and Gesture Recognition, Ljubljana, Slovenia) was to evaluate different kinship verification algorithms. For this task, two datasets were made available and three possible experimental protocols (unsupervised, image-restricted, and image-unrestricted) were designed. Five institutions submitted their results to the evaluation: (i) Politecnico di Torino, Italy; (ii) LIRIS-University of Lyon, France; (iii) Universidad de Las Palmas de Gran Canaria, Spain; (iv) Nanjing University of Aeronautics and Astronautics, China; and (v) Bar Ilan University, Israel. Most of the participants tackled the image-restricted challenge and experimental results demonstrated better kinship verification performance than the baseline methods provided by the organizers.

92 citations


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"Self-similarity representation of W..." refers background in this paper

  • ...75% which was comparatively better than Local Binary Pattern (LBP) [1], Histogram of Gradients (HOG) [4], Principal Component Analysis (PCA) [8], and Linear Embedding (LE) [2]....

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TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
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  • ...Therefore, Difference of Gaussian (DoG) approach [7] has been applied to extract these features using the steps below....

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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.
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"Self-similarity representation of W..." refers background in this paper

  • ...75% which was comparatively better than Local Binary Pattern (LBP) [1], Histogram of Gradients (HOG) [4], Principal Component Analysis (PCA) [8], and Linear Embedding (LE) [2]....

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