Eye detection based on improved ad AdaBoost algorithm

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.