Vérification de la parenté entre deux personnes par aprentissage automatique

Le domaine de la vérification de la parenté a attiré beaucoup d’attention ces dernières années en raison de sa capacité à améliorer les systèmes biométriques en tant que biométrique souple pour la vérification du visage (traits de parenté) et un rôle important dans de nombreuses applications de la société (vérification de la parenté). Parmi ces applications, citons la création d’arbres généalogiques, l’organisation d’albums de famille, l’annotation d’images, la recherche d’enfants disparus et la criminalistique. Bien qu’un Le test ADN est le moyen le plus fiable pour la vérification de la parenté, il ne peut pas être utilisé dans de nombreuses situations. La vérification automatique de la parenté à partir d’images faciales peut être réalisée à titre d’exemple dans les scènes de vidéosurveillance. Dans cette thèse, la vérification de la parenté faciale sur les images faciales est étudiée. À cette fin, nous commençons avec les approches précédemment proposées telles que les méthodes de vérification de parenté basées sur les fonctionnalités, les méthodes de vérification de parenté basées sur l’apprentissage métrique et les méthodes de vérification de parenté basées sur l’apprentissage profond par convolution. En outre, le système général de vérification de la parenté faciale est présenté, les défis et les mesures des caractéristiques sont mentionnés. De plus, les différents termes d’évaluation sont illustrés. Conclusion avec les approches proposées et les résultats obtenus sur diverses bases de données. Les systèmes proposés comprennent trois phases principales comme suit: 1) extractions de caractéristiques; 2) analyse des transformations du sous-espace; 3) décision de vérification de la parenté. Le but de l’extraction de traits est d’extraire des représentations discriminantes d’images faciales. Cette phase est importante car les traits de parenté sont très sensibles aux environ- nements non contraints (i.e. images capturées dans des environnements non contrôlés sans aucune restriction termes de pose, d’éclairage, d’arrière-plan, d’expression et d’occlusion partielle). En outre, cela peut affecter la performance de décision finale du système. La phase d’analyse des transformations du sous-espace extrait et sélectionne les traits du visage les plus attrayants et discriminants. Par conséquent, la les caractéristiques sont extraites par une projection des données originales (caractéristiques) de la phase précédente pour obtenir une meilleure discrimination et prendre des décisions plus précises. Dans la dernière phase, la similarité cosinus est utilisée comme la meilleure métrique compatible avec les méthodes d’analyse discriminante (méthodes d’analyse des transformations de sous-espace) et la vérification de parenté. La métrique finale entre deux images faciales est comparée à un seuil pour décider si les images faciales de la paire proviennent ou non de la même famille. Enfin, nos résultats montrent une grande amélioration pour la vérification de la parenté faciale sur les bases de données les plus grandes et les plus petites. En outre, les systèmes proposés ont obtenu une performance robuste et bonne et se comparent favorablement l’état de l’art approche. Les systèmes proposés sont également pratiques pour les applications en temps réel.The kinship verification field attracted much attention in the few past years due to its capacity to improve biometrics systems as a soft biometric for face verification (kinship traits) and an important role in many society applications (kinship verification). Among these applications include the creation of family trees, family album organization, image annotation, finding missing children and forensics. Although, the DNA test is the most trustworthy way for kinship verification, it cannot be used in many situations. Automatic kinship verification from facial images can exemplary be done in video surveillance scenes. In this thesis, facial kinship verification over facial images is studied. At this end, we start with the previously proposed approaches like features learning-based kinship verification methods, metric learning-based kinship verification methods, and convolutional deep learning-based kinship verification methods. Also, the general facial kinship verification system is presented, challenges and measures of characteristics are mentioned. Furthermore, the various evaluation terms are illustrated. Concluding with the proposed approaches and the obtained results on various databases. The proposed frameworks comprise of three main phases as follows: 1) features extractions; 2) subspace transformations analysis; 3) kinship verification decision. The aim of feature extraction is to extract discriminative representations of facial images. This phase is important since the kinship traits are very sensitive to the unconstrained environments (i.e. facial images captured under uncontrolled environments without any restrictions in terms of pose, lighting, background, expression, and partial occlusion). Also, it can affect the final decision performance of the framework. Subspace transformations analysis phase extract and select the more attractive and discriminative facial traits. Therefore, the features are extracted by a projection of the original data (features) of the previous phase to get better discrimination and make more precise decisions. In the last phase, cosine similarity is used as the best metric compatible with discriminant analysis methods (subspace transformations analysis methods) and kinship verification. The final metric between two facial images is compared to a threshold to decide if the pair facial images come from the same family or not. Finally, our results show great improvement for facial kinship verification on the largest and smallest databases. Also, a robust and good performance was achieved by the proposed systems and comparing favorably with the state of the art approaches. The proposed frameworks are also convenient for real-time applications

Kinship verification between two people by machine learning

The kinship verification field attracted much attention in the few past years due to its capacity to improve biometrics systems as a soft biometric for face verification (kinship traits) and an important role in many society applications (kinship verification). Among these applications include the creation of family trees, family album organization, image annotation, finding missing children and forensics. Although, the DNA test is the most trustworthy way for kinship verification, it cannot be used in many situations. Automatic kinship verification from facial images can exemplary be done in video surveillance scenes. In this thesis, facial kinship verification over facial images is studied. At this end, we start with the previously proposed approaches like features learning-based kinship verification methods, metric learning-based kinship verification methods, and convolutional deep learning-based kinship verification methods. Also, the general facial kinship verification system is presented, challenges and measures of characteristics are mentioned. Furthermore, the various evaluation terms are illustrated. Concluding with the proposed approaches and the obtained results on various databases. The proposed frameworks comprise of three main phases as follows: 1) features extractions; 2) subspace transformations analysis; 3) kinship verification decision. The aim of feature extraction is to extract discriminative representations of facial images. This phase is important since the kinship traits are very sensitive to the unconstrained environments (i.e. facial images captured under uncontrolled environments without any restrictions in terms of pose, lighting, background, expression, and partial occlusion). Also, it can affect the final decision performance of the framework. Subspace transformations analysis phase extract and select the more attractive and discriminative facial traits. Therefore, the features are extracted by a projection of the original data (features) of the previous phase to get better discrimination and make more precise decisions. In the last phase, cosine similarity is used as the best metric compatible with discriminant analysis methods (subspace transformations analysis methods) and kinship verification. The final metric between two facial images is compared to a threshold to decide if the pair facial images come from the same family or not. Finally, our results show great improvement for facial kinship verification on the largest and smallest databases. Also, a robust and good performance was achieved by the proposed systems and comparing favorably with the state of the art approaches. The proposed frameworks are also convenient for real-time applications

Vérification de la parenté entre deux personnes par aprentissage automatique

The kinship verification field attracted much attention in the few past years due to its capacity to improve biometrics systems as a soft biometric for face verification (kinship traits) and an important role in many society applications (kinship verification). Among these applications include the creation of family trees, family album organization, image annotation, finding missing children and forensics. Although, the DNA test is the most trustworthy way for kinship verification, it cannot be used in many situations. Automatic kinship verification from facial images can exemplary be done in video surveillance scenes. In this thesis, facial kinship verification over facial images is studied. At this end, we start with the previously proposed approaches like features learning-based kinship verification methods, metric learning-based kinship verification methods, and convolutional deep learning-based kinship verification methods. Also, the general facial kinship verification system is presented, challenges and measures of characteristics are mentioned. Furthermore, the various evaluation terms are illustrated. Concluding with the proposed approaches and the obtained results on various databases. The proposed frameworks comprise of three main phases as follows: 1) features extractions; 2) subspace transformations analysis; 3) kinship verification decision. The aim of feature extraction is to extract discriminative representations of facial images. This phase is important since the kinship traits are very sensitive to the unconstrained environments (i.e. facial images captured under uncontrolled environments without any restrictions in terms of pose, lighting, background, expression, and partial occlusion). Also, it can affect the final decision performance of the framework. Subspace transformations analysis phase extract and select the more attractive and discriminative facial traits. Therefore, the features are extracted by a projection of the original data (features) of the previous phase to get better discrimination and make more precise decisions. In the last phase, cosine similarity is used as the best metric compatible with discriminant analysis methods (subspace transformations analysis methods) and kinship verification. The final metric between two facial images is compared to a threshold to decide if the pair facial images come from the same family or not. Finally, our results show great improvement for facial kinship verification on the largest and smallest databases. Also, a robust and good performance was achieved by the proposed systems and comparing favorably with the state of the art approaches. The proposed frameworks are also convenient for real-time applications.Le domaine de la vérification de la parenté a attiré beaucoup d’attention ces dernières années en raison de sa capacité à améliorer les systèmes biométriques en tant que biométrique souple pour la vérification du visage (traits de parenté) et un rôle important dans de nombreuses applications de la société (vérification de la parenté). Parmi ces applications, citons la création d’arbres généalogiques, l’organisation d’albums de famille, l’annotation d’images, la recherche d’enfants disparus et la criminalistique. Bien qu’un Le test ADN est le moyen le plus fiable pour la vérification de la parenté, il ne peut pas être utilisé dans de nombreuses situations. La vérification automatique de la parenté à partir d’images faciales peut être réalisée à titre d’exemple dans les scènes de vidéosurveillance. Dans cette thèse, la vérification de la parenté faciale sur les images faciales est étudiée. À cette fin, nous commençons avec les approches précédemment proposées telles que les méthodes de vérification de parenté basées sur les fonctionnalités, les méthodes de vérification de parenté basées sur l’apprentissage métrique et les méthodes de vérification de parenté basées sur l’apprentissage profond par convolution. En outre, le système général de vérification de la parenté faciale est présenté, les défis et les mesures des caractéristiques sont mentionnés. De plus, les différents termes d’évaluation sont illustrés. Conclusion avec les approches proposées et les résultats obtenus sur diverses bases de données. Les systèmes proposés comprennent trois phases principales comme suit: 1) extractions de caractéristiques; 2) analyse des transformations du sous-espace; 3) décision de vérification de la parenté. Le but de l’extraction de traits est d’extraire des représentations discriminantes d’images faciales. Cette phase est importante car les traits de parenté sont très sensibles aux environ- nements non contraints (i.e. images capturées dans des environnements non contrôlés sans aucune restriction termes de pose, d’éclairage, d’arrière-plan, d’expression et d’occlusion partielle). En outre, cela peut affecter la performance de décision finale du système. La phase d’analyse des transformations du sous-espace extrait et sélectionne les traits du visage les plus attrayants et discriminants. Par conséquent, la les caractéristiques sont extraites par une projection des données originales (caractéristiques) de la phase précédente pour obtenir une meilleure discrimination et prendre des décisions plus précises. Dans la dernière phase, la similarité cosinus est utilisée comme la meilleure métrique compatible avec les méthodes d’analyse discriminante (méthodes d’analyse des transformations de sous-espace) et la vérification de parenté. La métrique finale entre deux images faciales est comparée à un seuil pour décider si les images faciales de la paire proviennent ou non de la même famille. Enfin, nos résultats montrent une grande amélioration pour la vérification de la parenté faciale sur les bases de données les plus grandes et les plus petites. En outre, les systèmes proposés ont obtenu une performance robuste et bonne et se comparent favorablement l’état de l’art approche. Les systèmes proposés sont également pratiques pour les applications en temps réel

Learning multi-view deep and shallow features through new discriminative subspace for bi-subject and tri-subject kinship verification

International audienceThis paper presents the combination of deep and shallow features (multi-view features) using the proposed metric learning (SILD+WCCN/LR) approach for kinship verification. Our approach based on an automatic and more efficient two-step learning into deep/shallow information. First, five layers for deep features and five shallow features (i.e. texture and shape), representing more precisely facial features involved in kinship relations (Father-Son, Father-Daughter, Mother-Son, and Mother-Daughter) are used to train the proposed Side-Information based Linear Discriminant Analysis integrating Within Class Covariance Normalization (SILD+WCCN) method. Then, each of the features projected through the discriminative subspace of the proposed SILD+WCCN metric learning method. Finally, a Logistic Regression (LR) method is used to fuse the six scores of the projected features. To show the effectiveness of our SILD+WCNN method, we do some experiments on LFW database. In term of evaluation, the proposed automatic Facial Kinship Verification (FKV) is compared with existing ones to show its effectiveness, using two challenging kinship databases. The experimental results showed the superiority of our FKV against existing ones and reached verification rates of 86.20% and 88.59% for bi-subject matching on the KinFaceW-II and TSKinFace databases, respectively. Verification rates for tri-subject matching of 90.94% and 91.23% on the available TSKinFace database for Father-Mother-Son and Father-Mother-Daughter, respectively

Kinship verification from face images in discriminative subspaces of color components

International audienceAutomatic facial kinship verification is a challenging topic in computer vision due to its complexity and its important role in many applications such as finding missing children and forensics. This paper presents a Facial Kinship Verification (FKV) approach based on an automatic and more efficient two-step learning into color/texture information. Most of the proposed methods in automatic kinship verification from face images consider the luminance information only (i.e. gray-scale) and exclude the chrominance information (i.e. color) that can be helpful, as an additional cue, for predicting relationships. We explore the joint use of color-texture information from the chrominance and the luminance channels by extracting complementary low-level features from different color spaces. More specifically, the features are extracted from each color channel of the face image and fused to achieve better discrimination. We investigate different descriptors on the existing face kinship databases, illustrating the usefulness of color information, compared with the gray-scale counterparts, in seven various color spaces. Especially, we generate from each color space three subspaces projection matrices and then score fusion methodology to fuse three distances belonging to each test pair face images. Experiments on three benchmark databases, namely the Cornell KinFace, the KinFaceW (I & II) and the TSKinFace database, show superior results compared to the state of the art

Kinship verification based deep and tensor features through extreme learning machine

Abstract Checking the kinship of facial images is a difficult research topic in computer vision that has attracted attention in recent years. The methods suggested so far are not strong enough to predict kinship relationships only by facial appearance. To mitigate this problem, we propose a new approach called Deep-Tensor+ELM to kinship verification based on deep (VGG-Face descriptor) and tensor (BSIF-Tensor & LPQ-Tensor using MSIDA method) features through Extreme Learning Machine (ELM). While ELM aims to deal with small size training features dimension, deep and tensor features are proven to provide significant enhancement over shallow features or vector-based counterparts. We evaluate our proposed method on the largest kinship benchmark namely FIW database using four Grandparent-Grandchild relations (GF-GD, GF-GS, GM-GD and GM-GS). The results obtained are positively compared with some modern methods, including those that rely on deep learning

Tensor cross-view quadratic discriminant analysis for kinship verification in the wild

Abstract This paper presents a new Tensor Cross-view Quadratic Discriminant Analysis (TXQDA) method based on the XQDA method for kinship verification in the wild. Many researchers used metric learning methods and have achieved reasonably good performance in kinship verification, none of these methods looks at the kinship verification as a cross-view matching problem. To tackle this issue, we propose a tensor cross-view method to train multilinear data using local histograms of local features descriptors. Therefore, we learn a hierarchical tensor transformation to project each pair face images into the same implicit feature space, in which the distance of each positive pair is minimized and that of each negative pair is maximized. Moreover, TXQDA was proposed to separate the multifactor structure of face images (i.e. kinship, age, gender, expression, illumination and pose) from different dimensions of the tensor. Thus, our TXQDA achieves better classification results through discovering a lowdimensional tensor subspace that enlarges the margin of different kin relation classes. Experimental evaluation on five challenging databases namely Cornell KinFace, UB KinFace, TSKinFace, KinFaceW-II and FIW databases, show that the proposed TXQDA significantly outperforms the current state of the art

Multi-view deep features for robust facial kinship verification

Abstract Automatic kinship verification from facial images is an emerging research topic in machine learning community. In this paper, we proposed an effective facial features extraction model based on multi-view deep features. Thus, we used four pre-trained deep learning models using eight features layers (FC6 and FC7 layers of each VGG-F, VGG-M, VGG-S and VGG-Face models) to train the proposed Multilinear Side-Information based Discriminant Analysis integrating Within Class Covariance Normalization (MSIDA + WCCN) method. Furthermore, we show that how can metric learning methods based on WCCN method integration improves the Simple Scoring Cosine similarity (SSC) method. We refer that we used the SSC method in RFIW’20 competition using the eight deep features concatenation. Thus, the integration of WCCN in the metric learning methods decreases the intra-class variations effect introduced by the deep features weights. We evaluate our proposed method on two kinship benchmarks namely KinFaceW-I and KinFaceW-II databases using four Parent-Child relations (Father-Son, Father-Daughter, Mother-Son and Mother-Daughter). Thus, the proposed MSIDA + WCCN method improves the SSC method with 12.80% and 14.65% on KinFaceW-I and KinFaceW-II databases, respectively. The results obtained are positively compared with some modern methods, including those that rely on deep learning