Gender classification plays a significant role in many applications such as video surveillance and commercial advertisement. Human face shows us various information such as age, race, identity, gender, emotion and so on. We can obtain this facial information through various techniques such as human face tracking and detection, face recognition, gender classification, emotion recognition, age estimation and so on.
In this thesis, we propose a new method of gender classification based on run-lengths histograms. The proposed method uses a run-length histogram to record the position information of pixels and efficiently improve the recognition rate, which is suitable for a big-data multimedia database. The experimental results show that the proposed method can achieve a better accuracy than that of multi-scale based method.
