Advances in Neural Networks - ISNN 2006: Third International by Do-Hyeon Kim, Eui-Young Cha, Kwang-Baek Kim (auth.), Jun PDF

By Do-Hyeon Kim, Eui-Young Cha, Kwang-Baek Kim (auth.), Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin (eds.)

ISBN-10: 3540344373

ISBN-13: 9783540344377

This publication and its sister volumes represent the court cases of the 3rd overseas Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China in the course of might 28–31, 2006. After a winning ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN grew to become a well-established sequence of meetings on neural computation within the area with turning out to be recognition and bettering caliber. ISNN 2006 obtained 2472 submissions from authors in forty three international locations and areas (mainland China, Hong Kong, Macao, Taiwan, South Korea, Japan, Singapore, Thailand, Malaysia, India, Pakistan, Iran, Qatar, Turkey, Greece, Romania, Lithuania, Slovakia, Poland, Finland, Norway, Sweden, Demark, Germany, France, Spain, Portugal, Belgium, Netherlands, united kingdom, eire, Canada, united states, Mexico, Cuba, Venezuela, Brazil, Chile, Australia, New Zealand, South Africa, Nigeria, and Tunisia) throughout six continents (Asia, Europe, North the USA, South the US, Africa, and Oceania). in keeping with rigorous studies, 616 fine quality papers have been chosen for booklet within the lawsuits with the reputation expense being below 25%. The papers are geared up in 27 cohesive sections masking all significant issues of neural community learn and improvement. as well as the varied contributed papers, ten exotic students gave plenary speeches (Robert J. Marks II, Erkki Oja, Marios M. Polycarpou, Donald C. Wunsch II, Zongben Xu, and Bo Zhang) and tutorials (Walter J. Freeman, Derong Liu, Paul J. Werbos, and Jacek M. Zurada).

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Extra info for Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II

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This is very important to the mobile phone because its memory is only a few KB. However, since the eye detector based on Haar-like feature only encode local gray intensive information of the eye, it is difficult to train an appropriate threshold to discriminate eye and eye-like images in the face regions such as eyebrows, thick frames of glasses etc. To solve this question, we train an eye-pair MLP classifier that is constructed using intrinsic geometrical and relative position information of eye-pair.

The negative samples are non-eye images, which are cropped from the face image due to eye searching area being restricted to the face. After the face region is located, we only search the upper area of the face region according to the prior knowledge about face model. The reduction of searching area can save the detection time and dramatically reduce the possible false eye candidates. Since the eye detector based on Haar-like Feature only encode local gray intensive information of the eye, it is difficult to train an appropriate threshold to discriminate eye and eye-like images in the face regions such as eyebrows, thick frames of glasses, etc.

Where f1 (x, w) > f2 (x, w) means that x can be recognized by RBF NN, f1 (x,w) ≥ τ1u ensures the output of NN corresponding to x is larger than the threshold τ1u , (S − x) means that in the next time of the training iteration we can remove the training pattern x out of training set S. In order to remove the recognized input patterns, a new error function (3) is proposed to train RBF NN classifier [max{0, τ1u − f1 (x, w)}2 + max{0, τ1L + f2 (x, w)}2 ] + E(w) = ∀x∈ω1 [max{0, τ2L + f1 (x, w)}2 + max{0, τ2u − f2 (x, w)}2 ].

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Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II by Do-Hyeon Kim, Eui-Young Cha, Kwang-Baek Kim (auth.), Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin (eds.)


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