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Rodrigues, Marcos; Kormann, Mariza; Tomek, Peter (2014)
Publisher: World Scientific and Engineering Academy and Society (WSEAS)
Languages: English
Types: Part of book or chapter of book

Classified by OpenAIRE into

This paper addresses the issue of real time gender classification through texture analysis. The purpose is to perform sensitivity analysis over a number of ROI-Regions of Interest defined over face images. The determination of the smaller ROI yielding robust classification results will be used for fast computation of texture parameters allowing gender classification to operate in real-time. Results demonstrate that the ROI comprising the front and the region of the eyes is the most reliable achieving classification accuracy of 88% for both male and female subjects using raw data and non-optimised extraction and classification algorithms. This is a significant result that will drive future research on optimisation of texture extraction and linear discriminant algorithms.
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    • [1] ADMOS (2013) Advertising Monitoring System Development for Outdoor Media Analytics, EC Grant Agreement 31552. [Online] Available at http://admos.eu
    • [2] T. Ahonen, A. Hadid, and M. Pietika¨inen. Face description with local binary patterns: Application to face recognition. TPAMI, 28(12):20372041, 2006.
    • [3] A. Webb and K. Copsey (2011) Statistical Pattern Recognition, 3rd edition, Wiley, 666pp.
    • [4] M. Pietika¨inen, A. Hadid, G. Zhao, and T. Ahonen. (2011) Computer Vision Using Local Binary Patterns. Springer.
    • [5] Ylioinas, J., Hadid, A., Pietika¨inen, M. (2011) Combining contrast and local binary patterns for gender classification. SCIA 17th Scandinavian Conference on Image Analysis.
    • [6] C. Shan (2012) Learning local binary patterns for gender classification on real-world face images, Patter Recognition Letters 33 (2012) 431-437.
    • [7] Y. Guo, G. Zhao, M. Pietika¨inen, and Z. Xu. Descriptor learning based on fisher separation criterion for texture classification. In Proc. ACCV10, 185-198, 2010.
    • [8] Lian, H., Lu, B. (2007) Multi-view gender classification using multi-resolution local binary patterns and support vector machines. Int J Neural Systems 17 (6), 479-487
    • [9] Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L. (2006) Gender classification based on boosting local binary pattern. In: Int Symp on Neural Networks.
    • [10] Viola, P., Jones, M. (2001) Rapid object detection using a boosted cascade of simple features. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 511518.
    • [11] Viola, P., Jones, M. (2004) Robust real-time face detection. Internat. J. Comput. Vision 57 (2), 137154.
    • [12] (2014) FEI Face Database. [Online] Available http://fei.edu.br/ cet/facedatabase.html (2011)
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  • HRZZ | Advanced 3D Perception for...
  • EC | ADMOS

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