Aiming at defects of traditional Adaboost algorithm and principal component analysis（ PCA） algorithm for face recognition in environment and pose and other nonbinding condition recognition rate is greatly reduced and the requirements of training samples in accordance with the Gauss distribution

a fusion of Adaboost and PCA and or related decision-making method is proposed. On the one hand

when the security mode is needed

the request of the whole negative sample is rejected

and the correct rate of recognition is guaranteed. On the other hand

face recognition in the need to open the visitor mode and decision-making

to reduce the loss of positive samples. On Sumsung 2440 embedded Linux platform

using the method of face detection

based on two decision-making methods

respectively meet the respective threshold. Experimental results show that the method works on embedded platform stably

suitable for promotion in intelligent home control and building automation and control.