Gender Classification based on Lossy Data Coding
This paper concerns the gender classification task of discriminating between images of faces of men and women from face images. In appearance-based approaches, the initial images are preprocessed (e.g. normalized) and input into classifiers. In this paper, we present a simple new criterion for classification, based on principles from lossy data compression. The criterion assigns a test sample to the class that uses the minimum number of additional bits to code the test sample, subject to an allowable distortion. This formulation induces several good effects on the resulting classifier. First, minimizing the lossy coding length in-duces a regularization effect which stabilizes the (implicit) density estimate in a small sample setting. Second, compression provides a uniform means of handling classes of varying dimension. The experimental results show that our methods outperformed SVMs with cross-validation in most of data sets.
Zhuowei Guan Ye Zhang
Institute of Image & Information Technology Harbin Institute of Technology Harbin, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
916-919
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）