FamilyGAN: Generating Kin Face Images Using Generative Adversarial Networks
23 Aug 2020-pp 297-311
TL;DR: In this article, a GAN-based approach was proposed to generate kin-images using Generative Adversarial Learning (GAN) for multiple kin-relations, such as parent-child and siblings.
Abstract: Automatic kinship verification using face images involves analyzing features and computing similarities between two input images to establish kin-relationship. It has gained significant interest from the research community and several approaches including deep learning architectures are proposed. One of the law enforcement applications of kinship analysis involves predicting the kin image given an input image. In other words, the question posed here is: “given an input image, can we generate a kin-image?” This paper attempts to generate kin-images using Generative Adversarial Learning for multiple kin-relations. The proposed FamilyGAN model incorporates three information, kin-gender, kinship loss, and reconstruction loss, in a GAN model to generate kin images. FamilyGAN is the first model capable of generating kin-images for multiple relations such as parent-child and siblings from a single model. On the WVU Kinship Video database, the proposed model shows very promising results for generating kin images. Experimental results show 71.34% kinship verification accuracy using the images generated via FamilyGAN.
Citations
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TL;DR: A comprehensive review of the state-of-the-art methods for Facial Kinship Verification (FKV) can be found in this paper , where the authors identify gaps in current research and discuss potential future research directions.
Abstract: Abstract The goal of Facial Kinship Verification (FKV) is to automatically determine whether two individuals have a kin relationship or not from their given facial images or videos. It is an emerging and challenging problem that has attracted increasing attention due to its practical applications. Over the past decade, significant progress has been achieved in this new field. Handcrafted features and deep learning techniques have been widely studied in FKV. The goal of this paper is to conduct a comprehensive review of the problem of FKV. We cover different aspects of the research, including problem definition, challenges, applications, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. In retrospect of what has been achieved so far, we identify gaps in current research and discuss potential future research directions.
8 citations
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TL;DR: In this paper , the authors leverage the pre-trained state-of-the-art face synthesis model, StyleGAN2, for kinship face synthesis, which can handle large age, gender and other attribute variations between the parents and their children.
Abstract: High-fidelity kinship face synthesis is a challenging task due to the limited amount of kinship data available for training and low-quality images. In addition, it is also hard to trace the genetic traits between parents and children from those low-quality training images. To address these issues, we leverage the pre-trained state-of-the-art face synthesis model, StyleGAN2, for kinship face synthesis. To handle large age, gender and other attribute variations between the parents and their children, we conduct a thorough study of its rich latent spaces and different encoder architectures for an optimized encoder design to repurpose StyleGAN2 for kinship face synthesis. The obtained latent representation from our developed encoder pipeline with stage-wise training strikes a better balance of editability and synthesis fidelity for identity preserving and attribute manipulations than other compared approaches. With extensive subjective, quantitative, and qualitative evaluations, the proposed approach consistently achieves better performance in terms of facial attribute heredity and image generation fidelity than other compared state-of-the-art methods. This demonstrates the effectiveness of the proposed approach which can yield promising and satisfactory kinship face synthesis using only a single and straightforward encoder architecture.
References
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