A Hybrid Approach for Gender Classification of Web Images

In recent times, gender recognition of facial images has achieved lots of attraction. It can be useful in many places e.g. security, web searching, human computer interaction etc. In this paper, an approach containing both face detection and gender classification tasks has been proposed. In face detection part, Haar features have been chosen to present appearance features along with Ada-Boost technique to target strong and powerful features in cascaded form. For gender classification, Bayesian Classifier has been used where image is analyzed in blocks/patches form. The blocking technique is same as used in DCT approach. Experimental results have shown that proposed approach is effective and robust with changes in pose (some degree), expressions and illumination.