Title :
Eye detection based on improved ad AdaBoost algorithm
Author :
Xiang, Benke ; Cheng, Xiaoping
Author_Institution :
Coll. of Comput. & Inf. Sci., Southwest Univ., Chongqing, China
Abstract :
Eye detection is an important step in eye tracking and eye state recognition. An improved AD AdaBoost algorithm for eye detection is proposed to slow the degradation in training step. Weight on negative samples which are classified correctly is released then the other samples´ weight is normalized to slow the expansion of weight on difficult samples. The experiment results show that the approach proposed is real time and has a higher detection accuracy.
Keywords :
eye; iris recognition; learning (artificial intelligence); AD AdaBoost algorithm; eye detection; eye state recognition; Classification algorithms; Computers; Degradation; Error analysis; Humans; Signal processing algorithms; Training; AD AdaBoost; Cascaded Classifier; Eye Detection;
Conference_Titel :
Signal Processing Systems (ICSPS), 2010 2nd International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-6892-8
Electronic_ISBN :
978-1-4244-6893-5
DOI :
10.1109/ICSPS.2010.5555731
