KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network

In this paper, we propose a kinship generator network that can synthesize a possible child face by analyzing his/her parent's photo. For this purpose, we focus on to handle the scarcity of kinship datasets throughout the paper by proposing novel solutions in particular. To extract robust features, we integrate a pre-trained face model to the kinship face generator. Moreover, the generator network is regularized with an additional face dataset and adversarial loss to decrease the overfitting of the limited samples. Lastly, we adapt cycle-domain transformation to attain a more stable results. Experiments are conducted on Families in the Wild (FIW) dataset. The experimental results show that the contributions presented in the paper provide important performance improvements compared to the baseline architecture and our proposed method yields promising perceptual results.Comment: Accepted to IEEE ICIP 201

Bias in Deep Learning and Applications to Face Analysis

Deep learning has fostered the progress in the field of face analysis, resulting in the integration of these models in multiple aspects of society. Even though the majority of research has focused on optimizing standard evaluation metrics, recent work has exposed the bias of such algorithms as well as the dangers of their unaccountable utilization.n this thesis, we explore the bias of deep learning models in the discriminative and the generative setting. We begin by investigating the bias of face analysis models with regards to different demographics. To this end, we collect KANFace, a large-scale video and image dataset of faces captured ``in-the-wild’'. The rich set of annotations allows us to expose the demographic bias of deep learning models, which we mitigate by utilizing adversarial learning to debias the deep representations. Furthermore, we explore neural augmentation as a strategy towards training fair classifiers. We propose a style-based multi-attribute transfer framework that is able to synthesize photo-realistic faces of the underrepresented demographics. This is achieved by introducing a multi-attribute extension to Adaptive Instance Normalisation that captures the multiplicative interactions between the representations of different attributes. Focusing on bias in gender recognition, we showcase the efficacy of the framework in training classifiers that are more fair compared to generative and fairness-aware methods.In the second part, we focus on bias in deep generative models. In particular, we start by studying the generalization of generative models on images of unseen attribute combinations. To this end, we extend the conditional Variational Autoencoder by introducing a multilinear conditioning framework. The proposed method is able to synthesize unseen attribute combinations by modeling the multiplicative interactions between the attributes. Lastly, in order to control protected attributes, we investigate controlled image generation without training on a labelled dataset. We leverage pre-trained Generative Adversarial Networks that are trained in an unsupervised fashion and exploit the clustering that occurs in the representation space of intermediate layers of the generator. We show that these clusters capture semantic attribute information and condition image synthesis on the cluster assignment using Implicit Maximum Likelihood Estimation.Open Acces

Family Relationship Analysis In Photos

Family relationship analysis has many potential applications, ranging from homeland security through to image search and social activity analysis. In our work, we present five computational problems for family relationship analysis in face photos. Studying these challenging problems is important and useful for semantic image understanding and social context extraction. In our study, the familial traits are learned from pairs of salient local facial parts using discriminative approaches. It is motivated by human perception studies on kinship recognition and the existence of familial traits through genetic inheritance. Second, kinship verification is performed on a pair of faces by integrating the familial traits based on confidence measures. Then, the generation recognition and specific family relationship recognition are explored. Finally, the separation of family and non-family group photos is studied based on a decision that combines multiple pair-wise kinship detections. An image database consisting of both family and non-family group photos is collected, and labeled at different levels of details. Experiments are performed on the database for all five tasks, based on different representations of the facial parts. Preliminary results show that the proposed problems can be addressed with a reasonably good performance. Our encouraging results may inspire more effort from the computer vision and image processing research community

Visual Transformation Aided Contrastive Learning for Video-Based Kinship Verification

Automatic kinship verification from facial information is a relatively new and open research problem in computer vision. This paper explores the possibility of learning an efficient facial representation for video-based kinship verification by exploiting the visual transformation between facial appearance of kin pairs. To this end, a Siamese-like coupled convolutional encoder-decoder network is proposed. To reveal resemblance patterns of kinship while discarding the similarity patterns that can also be observed between people who do not have a kin relationship, a novel contrastive loss function is defined in the visual appearance space. For further optimization, the learned representation is fine-tuned using a feature-based contrastive loss. An expression matching procedure is employed in the model to minimize the negative influence of expression differences between kin pairs. Each kin video is analyzed by a sliding temporal window to leverage short-term facial dynamics. The effectiveness of the proposed method is assessed on seven different kin relationships using smile videos of kin pairs. On the average, 93:65% verification accuracy is achieved, improving the state of the art. © 2017 IEEE