Abstract:
There are two main approaches to the problem of gender classification, Support Vector Machines (SVMs) and Adaboost learning methods, of which SVMs are better in correct rate but are more computation intensive while Adaboost ones are much faster with slightly worse performance. For possible real-time applications the Adaboost method seems a better choice. However, the existing Adaboost algorithms take simple threshold weak classifiers, which are too weak to fit complex distributions, as the hypothesis space. Because of this limitation of the hypothesis model, the training procedure is hard to converge. This paper presents a novel Look Up Table (LUT) weak classifier based Adaboost approach to learn gender classifier. This algorithm converges quickly and results in efficient classifiers. The experiments and analysis show that the LUT weak classifiers are more suitable for boosting procedure than threshold ones.
