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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
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
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ComputingMethodologies_PATTERNRECOGNITION
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • [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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