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

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Abstract

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.

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Acknowledgments

Portions of the research in this article use the FERET database of facial images collected under the FERET program.

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Authors and Affiliations

  1. Department of Electronic Engineering, Dalian University of Technology, Dalian Liaoning, 116023, China

    Huchuan Lu, Yingjie Huang & Yenwei Chen

  2. College of Information Science and Engineering, Ritsumeikan University, Kusatsu, 525-8577, Japan

    Yenwei Chen

  3. Department of Management, Dalian University of Technology, Dalian Liaoning, 116023, China

    Deli Yang

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  1. Huchuan Lu
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  2. Yingjie Huang
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  3. Yenwei Chen
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  4. Deli Yang
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Correspondence to Huchuan Lu.

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Lu, H., Huang, Y., Chen, Y. et al. Automatic gender recognition based on pixel-pattern-based texture feature. J Real-Time Image Proc 3, 109–116 (2008). https://doi.org/10.1007/s11554-008-0072-2

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  • DOI: https://doi.org/10.1007/s11554-008-0072-2

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