By Do-Hyeon Kim, Eui-Young Cha, Kwang-Baek Kim (auth.), Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin (eds.)
This publication and its sister volumes represent the complaints of the 3rd foreign Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China in the course of may possibly 28–31, 2006. After a profitable ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN turned a well-established sequence of meetings on neural computation within the sector with turning out to be recognition and enhancing caliber. ISNN 2006 got 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 United States, South the United States, Africa, and Oceania). in accordance with rigorous experiences, 616 top quality papers have been chosen for e-book within the court cases with the attractiveness cost being below 25%. The papers are geared up in 27 cohesive sections masking all significant issues of neural community examine and improvement. as well as the varied contributed papers, ten special 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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Additional info for Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II
Firstly, eye candidates are grouped into eye-pair candidates. Secondly, the true eye-pair can be selected from the eye-pair candidates by using the MLP classifier. Finally, according to classifying results we can obtain the position of eye center. Due to the geometrical relation information encoded by MLP, the disadvantage of eye detector based on Haar-like features can be overcome. 1 Geometrical Features and Training Data The results of prior detection process are a set of eye candidates’ rectangles and associated face rectangles.
1. The experimental results demonstrate that our eye location system can robustly cope with different light condition and achieve high detection rate on diverse test sets and can be implemented on a wide range of small resource-limit devices such as mobile phones. Fig. 1. Training and detection of Floatboost eye detector with MLP as post classifier 2 FloatBoost Classifiers AdaBoost, introduced in , provides an effective approach to address nonlinear classification of high dimensional data. The AdaBoost constructs the strong classifier as a combination of weak classifiers with proper coefficients.
The BioID database consists of 1520 images (384*286pixel). The JAFFE database consists of 213 images (256*256pixel). A relative error measure  is used to evaluate the precision of eye localization. Let dl , dr be Euclidean distance between the detection left(right) eye position and the marked left(right) eye position respectively, dlr be Euclidean distance between the marked left and right eye. Then the relative error is defined as follows: err=max(dl,dr)/dlr. 25, the eyes were deemed to found.