点击次数：

论文类型：期刊论文

发表时间：2008-03-01

发表刊物：JOURNAL OF REAL-TIME IMAGE PROCESSING

收录刊物：SCIE、EI

卷号：3

期号：1-2

页面范围：109-116

ISSN号：1861-8200

关键字：Gender recognition; PPBTF; Adaboost; Gabor filter; SVM classifier

摘要：A pixel-pattern-based texture feature (PPBTF) is proposed for real-time gender recognition. A gray-scale image is transformed into a pattern map where edges and lines are to be used for characterizing the texture information. On the basis of the pattern map, a feature vector is comprised the numbers of the pixels belonging to each pattern. We use the image basis functions obtained by principal component analysis (PCA) as the templates for pattern matching. The characteristics of the feature are comprehensively analyzed through an application to gender recognition. Adaboost is used to select the most discriminative feature subset, and support vector machine (SVMs) is adopted for classification. Performed on frontal images from FERET database, the comparisons with Gabor show that PPBTF is a significant facial representation, quite effective and speedier in computation.