Using Local Binary Patterns And Optimized Features Selection For Gender Classification

• Main Authors: Po-Chen Kuo, 郭柏辰
• Other Authors: 徐麗蘋
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/18100611384528483360

碩士 === 中山醫學大學 === 醫學資訊學系碩士班 === 104 === Gender classification is a very important issue. Its application is very broad, such as monitoring systems and billboards. If this technology can be applied in daily life, life will be become more interesting and convenient. Feature extraction is the key step of gender classification. In this paper, we present a method which efficiently classifies gender by extracting the key optimized features. We have used Local Binary Pattern (LBP) to extract facial features. As LBP features contain many redundant features, Particle Swarm Optimization (PSO) was applied to select optimized features. Experimental results showed that our method outperforms other methods in terms of accuracy and time complexity.