Kinship verification in childhood images using curvelet transformed features

Abstract

Kinship verification poses a formidable challenge due to significant age disparities between parents and children, resulting in diminished facial resemblances and consequently reduced model accuracy. In response, we introduce a novel methodology focusing on childhood imagery, wherein images of both parents and children during their early stages are leveraged, leading to more pronounced feature similarities. Our approach introduces a novel feature descriptor utilizing the curvelet transform (CLT) to extract facial descriptors from sub-bands, effectively capturing unique facial features. Additionally, we employ a convolutional neural network (CNN) to extract texture information, further enhancing the discriminative power of our method. Statistical measures are subsequently applied to evaluate image similarity, with a dedicated training phase utilized to determine an optimal threshold for kinship classification during the testing phase. Extensive evaluations conducted across multiple databases, including KINFACEW-I, II, FIW, TSKINFACE, UBKinface, and childhood image dataset, showcase the superior performance of our proposed model compared to existing state-of-the-art approaches. Our methodology not only enhances kinship verification accuracy but also underscores the significance of childhood images in mitigating age-related challenges within the domain.