• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine

Vol. 7, No. 7, July 29, 2013
10.3837/tiis.2013.07.011, Download Paper (Free):

Abstract

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.


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Cite this article

[IEEE Style]
J. Yang, Y. Jiao, N. Xiong and D. Park, "Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine," KSII Transactions on Internet and Information Systems, vol. 7, no. 7, pp. 1705-1720, 2013. DOI: 10.3837/tiis.2013.07.011.

[ACM Style]
Jucheng Yang, Yanbin Jiao, Naixue Xiong, and DongSun Park. 2013. Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine. KSII Transactions on Internet and Information Systems, 7, 7, (2013), 1705-1720. DOI: 10.3837/tiis.2013.07.011.