A new deep model for family and non-family photo identification

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Research Article

Publication Title

Multimedia Tools and Applications


Human trafficking is a global issue of the world and the problems related to human trafficking remain unsolved. This paper presents a new method for the identification of photos of different types of families and non-families such that the method can assist investigation team to find a solution to such issue. We believe that parts of human beings are the main resources for representing family and non-family photos. Based on this intuition, we propose to segment hair, head, cloth, torso, and skin regions from each human in input photos by exploring a self-correlation for human parsing method. This step results in region of interest (ROI). Motivated by ability of deep learning models in solving complex issues and special property of MobileNet, which is light weight model, we further explore MobileNetv2 for the identification of photos of different families and non-families by considering ROI as the input. For the experiment of this work, we consider a dataset of ten classes, which include five family classes, namely, Couple, Nuclear Family, Multi-Cultural Family, Father–Child, Mother–Child and five more non-family classes, namely, Male Friends, Female Friends, Mixed Friends, Male Celebrity, Female Celebrity. The results of the proposed method are demonstrated by testing on our dataset of family and non-family photos classification. Comparative results with the existing methods show that our proposed method outperforms existing methods in terms of classification rate and F-Score.

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