Multi-view gender classification using symmetry of facial images
In this paper, we propose a multi-view gender classification system with a hierarchical framework using facial images as input. The front end of the framework is a classifier, which will properly divides the input images into several groups. To ease the data sparsity problem in the multi-view scenario, facial symmetry is used to reduce the number of views. Moreover, we adopt soft assignment when dividing the input data, which can reduce the errors caused by the boundary effect in hard assignment. Then for each group, we train a gender classifier, called an expert. These experts can be any commonly used classifiers, such as support vector machines or neural networks. In this step, facial components instead of the whole face are used to achieve higher robustness against variations caused by facial alignment, illumination and occlusions. Experimental results demonstrate that our framework significantly improves the performance.
gender classification facial symmetry hierarchical classifiers multi-view facial images multiple kernel learning