Combining Center-Symmetric Local Ternary Pattern and Local Sign Directional Pattern for Gender Classification
Chih‐Chin Lai, Yen-Chun Huang · Frontiers in artificial intelligence and applications · 2015
Determining the gender of a person in a given image or video is an interesting problem. This is a two-class pattern recognition task which is very useful in many potential real-life applications. As human faces provide important visual information for gender perception, a large number of studies have investigated gender classification from face perception. In this paper, we present a method which uses Center-Symmetric Local Ternary Pattern and Local Sign Directional Pattern for feature extraction to identify the gender from the facial images. The classification is performed by using a support vector machine, which had been shown to be superior to traditional pattern classifiers in gender classification problem. Experimental results on the FERET database are provided to illustrate the proposed approach is an effective method, compared to other similar methods.