Abstract
Kinship verification aims to determine whether two given persons are blood relatives. This technique can be leveraged in many real-world scenarios, such as finding missing people, identification of kinship in forensic medicine, and certain types of interdisciplinary research. Most existing methods extract facial features directly from given images and examine the full set of features to verify kinship. However, most approaches are easily affected by the age gap among faces, with few methods taking age into account. This paper accordingly proposes an Age-Invariant Adversarial Feature learning module (AIAF), which is capable of factoring in full facial features to create two uncorrelated components, i.e., identity-related features and age-related features. More specifically, we harness a type of adversarial mechanism to make the correlation between these two components as small as possible. Moreover, to pay different attention to identity-related features, we present an Identity Feature Weighted module (IFW). Only purified identity features are fed into the IFW module, which can assign different weights to the features according to their importance in the kinship verification task. Experimental results on three public popular datasets demonstrate that our approach is able to capture useful age-invariant features, i.e., identity features, and achieve significant improvements compared with other state-of-the-art methods on both small-scale and large-scale datasets.
Highlights
Kinship verification, which aims to determine whether a pair of input images are those of blood relatives, is a challenging but exciting task
We proposed an Age-Invariant Adversarial Feature Learning approach for kinship verification, which comprises two modules: Adversarial Feature Learning module (AIAF) and Identity Feature Weighted module (IFW)
Identity classification, age classification, and Adversarial Canonical Correlation Regularizer modules are jointly optimized, which ensures that identity features are not correlated with age features
Summary
Kinship verification, which aims to determine whether a pair of input images are those of blood relatives, is a challenging but exciting task. It is beneficial to many real-world scenarios if it can be used wisely, including finding missing people, human trafficking, paternity tests and identification of kinship in forensic medicine, and even interdisciplinary research such as the relationship analysis of historical figures Many approaches, such as those based on traditional machine learning [1,2,3,4,5,6] or emerging deep learning [7,8,9,10,11,12,13,14], have been proposed for kinship verification and have achieved great performance. It is a two-stage approach, which increases computational costs
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