关键词: 人脸检测, Haar-like特征, AdaBoost算法, 双阈值, 优化加权参数

Abstract: Existed single threshold methods will cause highly false recognition rates in face detection. This paper proposes a fast AdaBoost algorithm based on optimized weighting parameter. Firstly, algorithm changes the solving formula of weighted parameter to get low false alarm rates even under the premise of low false recognition rates. Secondly, dual-threshold is obtained by calculating the feature-value curve. Finally, the detected dual thresholds are used to form weak classifiers, which can form an ensemble classifier. Experimental results show that it not only improves the accuracy of detection, but also ameliorates the training and detecting time since dual-threshold can decrease the times of searching.

Key words: face detection, Haar-like feature, AdaBoost algorithm, dual-threshold, optimized weighting parameter