Automatic gender recognition based on pixel-pattern-based texture feature

作者：Lu Huchuan^*; Huang Yingjie; Chen Yenwei; Yang Deli

来源：Journal of Real-Time Image Processing, 2008, 3(1-2): 109-116.

DOI：10.1007/s11554-008-0072-2

• Gender recognition
• PPBTF
• Adaboost
• Gabor filter
• SVM classifier

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.

• 出版日期2008-3
• 单位大连理工大学