An Optimized Face Detection Based on Adaboost Algorithm
Hao Zeng, Qin Feng, Lin Kaidong · 2018
In the real face detection, AdaBoost based algorithm usually has a higher false positive rate and loss rate. But it faces with the problem of long training time, susceptible to face deflection, obstruction and other factors. In view of the above problems, an improved face detection algorithm is proposed, which can reduce the training time and improve the training speed by using the feature processing, and the detection rate is improved by introducing the skin color detection based on YCgCr color space. Through experimental testing, the proposed algorithm can solve the occlusion, angle, light and other problems to a certain extent.