性別辨識在近年來電腦視覺的領域當中，是一個非常重要的議題，其應用面非常廣泛，像是監視系統和針對性別來進行服務或統計之廣告看板等等，另外若是能在日常生活中確實應用此技術，生活將會變得更加有趣和方便。性別辨識主要透過人臉影像進行臉部特徵擷取，再將取出的特徵經由分類器進行性別的分類。因此我們針對這項議題，提出了一套新的人臉性別辨識方法：我們把資料庫中的人臉影像經過預先處理，並用LBP來計算臉部的LBP特徵分布及男性特有的鬍子特徵，再以Particle Swarm Optimization(PSO)來選出其中最適合分類性別的特徵，最後使用SVM分類器根據PSO找出的LBP特徵及鬍子特徵來進行性別判定。實驗結果顯示我們的方法相較於其他的辨識方法，有更好的辨識效能。
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.
