Cubic norm and kernel-based bi-directional PCA: toward age-aware facial kinship verification

A recent challenge in computer vision is exploring the cardinality of a relationship among multiple visual entities to answer questions like whether the subjects in a photograph have a kin relationship. This paper tackles kinship recognition from the aging viewpoint in which the system could find the parent of a child where the input image of the parent belongs to the age range that is lower than the child is. Technical contributions of this research are twofold. (1) An efficient discriminative feature space is constructed by proposing kernelized bi-directional PCA to form a topological cubic feature space. Cubic feature space in conjunction with the introduced cubic norm is used to solve the kinship problem. (2) To fill the gap of aging effect in finding a kin relation, a semi-supervised learning paradigm is proposed. To do this, first, the pooling layer of a convolutional neural network is modified to do a soft pooling. Then, the last pooling layer, as a rich feature vector, is fed into density-based spatial clustering of applications with noise algorithm. This pre-classification phase would be useful when there is no aggregation on how many classes should be used in the age group estimation task. Finally, by adding kernel computation to sparse representation classifier, the age classification is done. Evaluation of the proposed method on five publicly available facial kinship datasets shows the superiority of the proposed method over both the state-of-the-art kinship verification methods and what is known as human decision-making.