A Face Detection Method Based on Image Processing and Improved Adaptive Boosting Algorithm

Abstract

In face detection, the Adaptive Boosting (AdaBoost) algorithm consumes a long training time and faces a high false positive rate. To solve these problems, this paper puts forward an improved AdaBoost face detection method. Firstly, the original image was preprocessed to eliminate the effects of light and noise, improving the image detection effect. Next, a dual threshold weak classifier was designed to replace the single threshold weak classifier. The designed classifier identifies thresholds more accurately and reduce the number of threshold searches, making the algorithm faster in convergence and more efficient in training and detection. Then, the authors optimized the weighting coefficient formula of weak classifiers, focusing on the recognition ability of positive samples and the reliability of weak classifiers. Through the optimization, the algorithm can achieve a low false alarm rate (FAR) under a given low false recognition rate (FRR). After that, two thresholds were used to classify the error range of samples. To increase the weights of large error samples, the original weights of samples were multiplied with different weighting coefficients. In this way, the abnormal samples are more likely to be detected in the next round of training. Simulation results show that the proposed face detection algorithm boasts a high detection accuracy, and consumes a short time in training and detection.