Aiming at problem that training time of ordinary Adaboost algorithm is long and under complex background such as dark light

multi angle

multi pose

the recognition rate of face detection is low

propose an improved Adaboost algorithm based on skin color detection and geometric features. The algorithm screens out candidate face regions based on skin color detection

at the same time

a new non-symmetric Haar feature is used to train the classifier

which further enhances detection performance and promote robustness and tolerance in the context of complexity. In the experiment

the algorithm is applied to an embedded system

the results show that the algorithm can improve the robustness and tolerance of face detection in all kinds of complex background

and the error recognition rate is further reduced

in embedded face detection system and has good portability and practicability.