Eye Detection Based on Improved AdaBoost Algorithm
Yang Li · 2018
Focused on the issue that the detection error rate of the current eye detection method is relatively high, and when the AdaBoost algorithm is used to train the classifier, it is easy to appear the phenomenon of weight imbalance. A new eye detection method based on the improved AdaBoost algorithm is proposed. First, the AdaBoost algorithm is applied to the detection of human eyes. Then the reason for the imbalance of weights in training of AdaBoost algorithm is analyzed, and the concept of missing detection rate is introduced to improve the weight updating process of AdaBoost algorithm. The experimental results show that the improved AdaBoost algorithm ensured the sample weight distribution balance and improve the accuracy in the training process; eye detection based on the improved AdaBoost algorithm effectively maintains a high detection rate and inhibits the detection error rate, makes detection more accurate.