Face gender recognition with halftoning-based adaboost classifiers
Jing-Ming Guo, Chen-Chi Lin, Che‐Hao Chang, Yun-Fu Liu · 2013
This paper presents a new face gender recognition scheme by enjoying the benefit from the dot diffusion among weak classifiers in recognition phase for a low resolution and non-aligned thumbnail image. The main problem of the former Adaboost approaches is that each weak classifier simply offers a binary decision, which fails to compensate the decision error by diffusing it to the rest weak classifiers. To cope with this, this work exploits the dot-diffused-based Adaboost to solve this problem. As documented in the experimental results, with the examination of Feret and CMU databases, this paper has shown that the proposed scheme is an effective candidate in improving the recognition accuracy rate and the efficiency of the overall system process for face gender recognition.