We proposed a novel boosting family classification algorithm called SODA-Boosting (where SODA stands for Second Order Discriminant Analysis). SODA-Boosting aims at efficiently learning discriminative weak classifiers, based on linear features that can be computed in closed-form. It extends the idea of our MRC-Boosting algorithm and can serve as a generic binary classifier.

As an application, SODA-Boosting was employed in image based gender recognition. Experimental results on publicly available FERET database showed that SODA-Boosting achieved accuracy comparable to state-of-the-art approaches, and demonstrated superior performance compared to relevant boosting based algorithms. The algorithm has been integrated into our facial recognition software, capable of classifying gender in real-time from webcam feeds.

This work was featured by the National Geographic Channel in its primetime program ”How It Was: Secrets of Mona Lisa” (aired on March 18, 2008).