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A gender classification scheme based on multi-region feature extraction and information fusion for unconstrained images

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Abstract

Since gender classification has been interesting in many applications, we proposed a gender classification scheme based on multi-region feature extraction and information fusion in the paper. The proposed gender classification scheme is composed of three parts: pre-processing, multi-region feature extraction, and gender classifier. Before extracting useful information from multiple regions in a facial image, face detection and face orientation correction are performed in the pre-processing. Multi-region feature extraction measures three kinds of features from eyes, internal face, and hair. Since the three kinds of features have their particular properties, a classifier based on decision-level information fusion is utilized to combine these features for gender classification. To evaluate the proposed scheme, a large number of unconstrained images containing different-size faces are captured by using a low-cost webcam and digital cameras. Experimental results show that our proposed scheme can detect facial regions and the location of eyes well. Furthermore, the accuracy of the proposed gender classification scheme is higher than 96 %. These experimental results demonstrate that the proposed scheme can deal with unconstrained images for gender classification.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Da-Yeh University, Changhua County, Taiwan

    Guo-Shiang Lin

  2. Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan

    Min-Kuan Chang & Yu-Jui Chang

  3. Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan

    Chia-Hung Yeh

Authors
  1. Guo-Shiang Lin
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  2. Min-Kuan Chang
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  3. Yu-Jui Chang
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  4. Chia-Hung Yeh
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Correspondence to Guo-Shiang Lin or Min-Kuan Chang.

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Lin, GS., Chang, MK., Chang, YJ. et al. A gender classification scheme based on multi-region feature extraction and information fusion for unconstrained images. Multimed Tools Appl 75, 9775–9795 (2016). https://doi.org/10.1007/s11042-015-2797-9

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  • DOI: https://doi.org/10.1007/s11042-015-2797-9

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