Spatial arrangements of people in a photo reflect how people regard themselves in a group. Comparing spatial arrangements of subjects in two group photos can help computer understand similar social semantics and events. In this paper, we incorporate gender information with spatial arrangements of subjects to assess the visual similarity between two group photos. For each group photo, detected faces are represented by a spatial face context. Then, corresponding vertices of spatial face contexts of two group photos are retrieved by graph matching. Gender information and relative face positions are applied to the graph matching results to compute the similarity score. The experimental results show that our method can identify group photos with similar genders of subjects and spatial arrangements compared to the state-of-the-art methods.
In this paper, we propose a new temporal coherent face descriptor for video gender recognition. The proposed face descriptor is constructed from detected faces of continuous video frames. Because it describes detected faces under variant changes in continuous video frames and provides a unified feature description, face normalization and alignment processes can be avoided during gender recognition. Based on the face descriptor, a support vector machine classifier is applied to identify the gender of the subject in the videos. As shown in the experiments, our method not only achieves better results compared to the state-ofthe-art methods but also the real-time performance for video processing.
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