Face Kinship Verification Based VGG16 and new Gabor Wavelet Features

Kinship verification from face images is a motivating field of study in computer vision, involving many researches works because-of its importance in many potential applications, such as forensics and finding missing children. This application of automatically determining whether persons share a blood re-lationship by examining their facial characteristics, i.e., features. In this work, we develop an efficient method named Hist-Gabor based on the histogram features extracted from basic Gabor wavelet in order to represent face images with high discriminate power. Indeed, we examine the use of deep features collected from a convolutional neural network model called VGG-face and shallow features by our new Gabor wavelet invoking a powerful dimensionality reduction method named Tensor Cross-view Quadratic Analysis (TXQDA). Empirically, our experiments demonstrate that the proposed approach outperforms the pre-vious state-of-the-art in the challenging datasets Cornell and TSKinFace.