TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine |
| 영문제목(English Title) |
Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine |
| 저자(Author) |
Jucheng Yang
Yanbin Jiao
Naixue Xiong
DongSun Park
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| 원문수록처(Citation) |
VOL 07 NO. 07 PP. 1705 ~ 1720 (2013. 07) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Extreme Learning Machine
Gender Recognition
Local Ternary Pattern
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| 파일첨부 |
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