Face Gender Classification on Consumer Images in a Multiethnic Environment

Abstract. In this paper, we target at face gender classification on consumer images in a multiethnic environment. The consumer images are much more challenging, since the faces captured in the real situation vary in pose, illumination and expression in a much larger extent than that captured in the constrained environments such as the case of snapshot images. To overcome the nonuniformity, a robust Active Shape Model (ASM) is used for face texture normalization. The probabilistic boosting tree approach is presented which achieves a more accurate classification boundary on consumer images. Besides that, we also take into consideration the ethnic factor in gender classification and prove that ethnicity specific gender classifiers could remarkably improve the gender classification accuracy in a multiethnic environment. Experiments show that our methods achieve better accuracy and robustness on consumer images in a multiethnic environment.

• Type: Conference
• Year: 2009
• Where: ICB
• Authors: Wei Gao, Haizhou Ai
• Keywords: Consumer Images, Gender Classification, ICB 2009, Multiethnic Environment