Conference Paper

Robust Kinship Verification using Local Descriptors

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Abstract

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, we propose a novel approach based on Local Ternary Patterns (LTP) and the Multi-Level (ML) representation. Also, we investigate the effect of ML and Multi-Block (MB) representation for facial kinship verification , and the effect of different features representation. Moreover, the use of Fisher Score to reduce the number of features and the support vector machine (SVM) for the kinship classification. Our approach consists of six stages which are : (i) face preprocessing, (ii) features extraction, (iii) face representation (iv) pair features representation and normalization, (v) features selection and (vi) classification. The proposed approach is tested and analyzed on three publicly available databases (Cornell KinFace, UB Kin database, Familly 101, KinFac W-I and W-II). The obtained results are good comparable with the state-of-art approaches.

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... This work was tested on Cornell KinFace, family 101, KinFaceW-I and KinFaceW-II databases. Chergui et al. [8] proposed the used of another image descriptor called LTP with another face representation called ML for extracting selected facial features. Moreover, an approach based on mixed image descriptors with ML features representation was done by Chergui et al. [9], and in other work [10] they proposed kinship verification system based on the deep features applied to the same databases. ...
Article
The use of facial images in the kinship verification is a challenging research problem in soft biometrics and computer vision. In our work, we present a kinship verification system that starts with pair of facial images of the child and parent, then as a final result is determine whether two persons have a kin relation or not. our approach contains five steps as follows: (i) the face preprocessing step to get aligned and cropped facial images of the pair (ii), extracting deep features based on the deep learning model called Visual Geometry Group (VGG) Face, (iii) applying our proposed pair feature representation function alongside with a features normalization, (iv) the use of Fisher Score (FS) to select the best discriminative features, (v) decide whether there is a kinship or not based on the Support Vector Machine (SVM) classifier. We conducted several experiments to demonstrate the effectiveness of our approach that we tested on five benchmark databases (Cornell KinFace, UB KinFace, Familly101, KinFace W-I, and KinFace W-II). Our results indicate that our system is robust compared to other existing approaches.
... Chergui et al. [8] proposed approach besed on the LBP and BSIF descriptors and the PML features they applied their method on both Cornell KinFace, UB KinFace , KinFace W-I and W-II databases and in other work Chergui et al. [9] proposed a Discriminant Analysis for Facial Verification using Color Images they applied their method on three databases Cornell, UB KinFace and familly 101.and proposed LTP descriptor with ML face representation and fisher Score selection for kinship verification [10], and they proposed another approach based on the deep features of VGG-FACE descriptor, They applied their approach on five databases ( Cornell, UB KinFace, Familly 101, Kinface W-I and W-II) [11] . ...
Conference Paper
The kinship verification through facial images is ana ctive research topic due to its potential applications. In this paper, we propose an approach which takes two images as input then give kinship result (kinship / No-kinship) as an output. our approach based on the deep learning model (ResNet) for the feature extraction step, alongside with our proposed pair feature representation function and RankFeatures (Ttest) for feature selection to reduce the number of features finally we use the SVM classifier for the decision of kinship verification. The approach contains three steps which are: (1) face preprocessing, (2) deep features extraction and pair features representation (3) Classification. Experiments are conducted on five public databases. The experimental results show that our approach is comparable with existed approaches.
... Also, they proposed another approach in [3] based on the discriminant analysis for facial kinship verication using color images , they tested their method on Cornell Kin, UB Kin and Familly 101 databases. and proposed LTP descriptor with ML face representation and fisher Score selection for kinship verification [4]. ...
Conference Paper
Kinship verification is a challenging problem that recently attracted much interest in computer vision, this system has a number of applications such as organizing large collections of images and recognizing resemblances among humans and search for lost people. In this work, we propose a new method based on different descriptors mixed such as (LBP, LPQ, BSIF), and the Multi-Block (MB) representation. and we investigate the effect of different features representation for kinship verification, Moreover, the use of TTest to reduce the number of features and the support vector machine (SVM) for the kinship classification. Our approach consists of five stages : (1) features extraction , (2) face representation (3) features representation, (4) features selection and (5) classification. Our approach is tested on five datasets (Cornell, UB Kin Face, Familly 101, KinFac W-I and W-II). Our results are good comparable with other approaches.
Thesis
kinship verification through facial images is one of the most active areas of research in biometrics and computer vision, and has received increasing attention in recent years. It is a technique which aims to exploit the characteristics of the face to recognize the degree of kinship of two individuals from their facial images. Kinship verification has given rise to many and multiple applications, it constitutes the core of several already operational systems, these can range from simple systems such as photo album organization systems and historical and genealogical research, up to '' to larger and more complicated systems, such as missing family member systems, identification of wanted individuals and child smuggling systems. The researches in the field of kinship verification has given rise to many and multiple applications, it constitutes the core of several already operational systems, these can range from simple systems such as photo album organization systems and historical and genealogical research, up to more important and complicated systems, such as missing family member systems, identification of wanted persons and child smuggling systems. Our work is part of the kinship verification through facial images where we propose a kinship verification system which receives at its entry a pair of facial images (parents, children) to determine at its exit if two persons have a kin relation or not. The proposed approach involves six steps: 1. The preprocessing of the facial image in order to obtain aligned and cropped facial images. 2. Feature extraction based on the texture descriptors and deep learning models. 3. The face representation using the multi-level pyramid (PML) to increase the features number. 4. The feature representation function alongside with a features normalization. 5. The features reduction (projection or selection) to retain the best discriminative features. 6. The decision whether there is a kinship or not, and this, using a classifier (SVM) vector of machine support. The proposed approach has been tested on five reference databases (Cornell KinFace, UB KinFace, Family101, KinFace W-I and KinFace W-II). For each step, we carried out several experiments, in order to determine the best and most appropriate parameters. A comparison of the proposed method with other advanced methods clearly shows that the results obtained are clearly better and good. Keywords: Kinship verification, face representation, feature representation, feature reduction, Classification.
Conference Paper
The kinship verification through facial images is an active research topic due to its potential applications. In this paper, we propose an approach which takes two images as an input then give kinship result (kinship / No-kinship) as an output. The approach contains five steps which are : (1) face preprocessing, (2) deep features extraction, (3) pair features representation and normalization, (4) features selection, (5) kinship verification. Experiments are conducted on five public databases (Cornell KinFace, UB Kin database, Familly, KinFaceI, and KinFace-II). The experimental results shows that our approach is comparable with existed approaches.
Thesis
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The automatic verification of kinship is a challenging problem that recently attracted much interest in computer vision, the kinship verification has become an active research field due to its potential applications such as organizing photo albums and images annotation, recognizing resemblances among humans and finding of missing children. In this paper, we propose an approach which takes two images as an input then give kinship result (kinship / non-kinship) as an output.This approach based on the Local Phase Quantization (LPQ) and Local directional pattern (LDP) features descriptors and the ML (Multi-Level) representation for the kinship verification from facial images, this work consists six stages which are : (i) face preprocessing, (ii) features extraction, (iii) face representation (iv) pair features representation and normalization, (v) features selection and (vi) kinship verification. Experiments are conducted on four public databases (Cornell KinFace, UB Kin database, KinFace-I, and KinFace-II). The obtained results are good compared with state-of-the-art approaches.
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Kin relationship has been well investigated in psychology community over the past decades, while kin verification using facial images is relatively new and challenging problem in biometrics society. Recently, it has attracted substantial attention from biometrics society, mainly motivated by the relative characteristics that children generally resemble their parents more than other persons with respect to facial appearance. Unlike most previous supervised metric learning methods focusing on learning the Mahalanobis distance metric for kin verification, we propose in this paper a new Ensemble similarity learning (ESL) method for this challenging problem. We first introduce a sparse bilinear similarity function to model the relative characteristics encoded in kin data. The similarity function parameterized by a diagonal matrix enjoys the superiority in computational efficiency, making it more practical for real-world high-dimensional kinship verification applications. Then, ESL learns from kin dataset by generating an ensemble of similarity models with the aim of achieving strong generalization ability. Specifically, ESL works by best satisfying the constraints (typically triplet-based) derived from the class labels on each base similarity model, while maximizing the diversity among the base similarity models. Experiments results demonstrate that our method is superior to some state-of-the-art methods in terms of both verification rate and computational efficiency.
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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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Kinship verification from facial images is an interesting and challenging problem in computer vision, and there is very limited attempts on tackle this problem in the iterature. In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification. Motivated by the fact that interclass samples (without kinship relations) 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 kinship relations) 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. Lastly, 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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Because of the inevitable impact factors such as pose, expression, lighting and aging on faces, identity verification through faces is still an unsolved problem. Research on biometrics raises an even challenging problem--is it possible to determine the kinship merely based on face images? A critical observation that faces of parents captured while they were young are more alike their children's compared with images captured when they are old has been revealed by genetics studies. This enlightens us the following research. First, a new kinship database named UB KinFace composed of child, young parent and old parent face images is collected from Internet. Second, an extended transfer subspace learning method is proposed aiming at mitigating the enormous divergence of distributions between children and old parents. The key idea is to utilize an intermediate distribution close to both the source and target distributions to bridge them and reduce the divergence. Naturally the young parent set is suitable for this task. Through this learning process, the large gap between distributions can be significantly reduced and kinship verification problem becomesmore discriminative. Experimental results show that our hypothesis on the role of young parents is valid and transfer learning is effective to enhance the verification accuracy.
Harmonic rule for measuring the facial similarities among relatives
  • Cn Ravi Kumar
CN Ravi Kumar. Harmonic rule for measuring the facial similarities among relatives. Transactions on Machine Learning and Artificial Intelligence, 4(6):29, 2017.
Genealogical face recognition based on ub kinface database
  • Ming Shao
  • Siyu Xia
  • Yun Fu
Ming Shao, Siyu Xia, and Yun Fu. Genealogical face recognition based on ub kinface database. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on, pages 60-65. IEEE, 2011.