KinfaceNet: A New Deep Transfer Learning based Kinship Feature Extraction Framework

Advances in vision and deep learning have revolutionized feature extraction for face recognition and verification systems, yet, performing kinship verification from such features is still challenging. Ongoing research attempts to imitate a human by identifying features for kinship verification. In this paper, we propose KinfaceNet, a deep learning based kinship feature extractor, capable of extracting kinship features from a single input image independently without requiring its kin pair image. The base model of the method is adopted from face recognition domain which is then transfer learned in the domain of kinship by learning a distance mapping from face images to a compact Euclidean space where distances directly correspond to a measure of kinship similarity. Thus, unlike most of the works in deep learning based kinship domain, the extracted features can be used in many other applications such as image generation and family based clustering, etc. Training is performed by rearranging the data into classes of kin pairs and using a state-of-the-art triplet mining algorithm to address the unbalanced kinship data problem which causes overfitting. Also, one of the major advantages of our framework is that training can be performed on any face feature extractor model pre-trained on large face recognition data, thereby reducing training time by a considerable amount. Comparable verification accuracy is obtained from simple MLP network at only 20th epoch with KinfaceNet features extracted from the Family-In-the-Wild dataset, the largest in the wild kinship dataset available, as well as KinfaceW-I and II datasets.