Children Gender Recognition Under Unconstrained Conditions Based on Contextual Information

One of the biggest challenges faced by law enforcement entities in the present digital era, is fighting against online Child Sexual Abuse (CSA), due in particular to the massive amount of data that they receive for analysis. Pattern recognition system can provide an aid, e.g., to ease the identification of both the perpetrator and the victim of the crime. In particular, ancillary cues related the identity of the involved persons, like age, race or gender, can represent a significant aid for identification. These cues can be estimated using statistical classifiers on face features. In this work, we explore one of these ancillary cues, namely the gender. The research community has provided methods for gender recognition able to achieve good performance with adults. However, in the case of CSA, victims are minors (typically, very young children). Children gender recognition may be difficult even for humans, due to the lack of many gender-specific face traits usually present in adult faces.

Totally uncontrolled poses and illumination conditions, that might be found in CSA material, represent an additional issue. We propose to tackle this problem by the use of contextual information to complement face features used by traditional algorithms. In particular, we exploit the image context of the face, that is, the portion of the image surrounding the face. This is motivated by the usage that humans themselves make of face external information, such as the hair or earrings, to take decisions on this task. The proposed approach is tested on a novel data base of faces of children, collected from royalty-free stock-photography web sites, which show totally unconstrained conditions. The reported results are promising and set the way for a deeper study of the use of the face context for estimating ancillary identification cues.

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