Gender Recognition Using Complexity-Aware Local Features

We propose a gender classifier using two types of local features, the gradient features which have strong discrimination capability on local patterns, and the Gabor wavelets which reflect the multi-scale directional information. The Real Ad a Boost algorithm with complexity penalty term is applied to choose meaningful regions from human face for feature extraction, while balancing the discriminative capability and the computation cost at the same time. Linear SVM is further utilized to train a gender classifier based on the selected features for accuracy evaluation. Experimental results show that the proposed approach outperforms the methods using single feature. It also achieves comparable accuracy with the state-of-the-art algorithms on both controlled datasets and real-world datasets.