Learning a Genetic Measure for Kinship Verification Using Facial Images

Lu Kou,Min Xu,Yuanyuan Shang,Xiuzhuang Zhou

doi:10.1155/2015/472473

Lu Kou, Min Xu + Show 2 more

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https://doi.org/10.1155/2015/472473

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Abstract

Motivated by the key observation that children generally resemble their parents more than other persons with respect to facial appearance, distance metric (similarity) learning has been the dominant choice for state-of-the-art kinship verification via facial images in the wild. Most existing learning-based approaches to kinship verification, however, are focused on learning a genetic similarity measure in a batch learning manner, leading to less scalability for practical applications with ever-growing amount of data. To address this, we propose a new kinship verification approach by learning a sparse similarity measure in an online fashion. Experimental results on the kinship datasets show that our approach is highly competitive to the state-of-the-art alternatives in terms of verification accuracy, yet it is superior in terms of scalability for practical applications.

Highlights

Due to the fact that rich human characteristics, such as gender, identity, expression, and ethnicity, can be effectively extracted from facial images, a variety of face analysis problems, ranging from face recognition, facial expression recognition, and gender estimation to age estimation, have been extensively studied over the past decades [1]
We propose a new kinship verification approach by learning a sparse similarity measure in an online fashion
Kinship verification using facial images, is a relatively new and challenging problem in biometrics [2], which is mainly motivated by the phenomenon that children generally look like their parents more than other people due to the kinship relation

Summary

Introduction

Due to the fact that rich human characteristics, such as gender, identity, expression, and ethnicity, can be effectively extracted from facial images, a variety of face analysis problems, ranging from face recognition, facial expression recognition, and gender estimation to age estimation, have been extensively studied over the past decades [1]. Existing methods for kinship verification are either feature-based [2, 13, 15,16,17, 19] or learning-based [10, 11, 14, 18, 20] The former aims for extraction of the discriminative feature from facial images to characterize genetic property in human appearance. Learning-based approaches, are focused on learning a genetic measure via training data based on some discriminative learning technologies, such as subspace learning and distance metric learning, to improve the separability of facial images for kinship verification. Our proposed approach is able to achieve highly competitive verification accuracy to state-of-the-art kinship verification method and is superior in terms of scalability, making it more scalable for practical applications with ever-growing amount of data

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