In order to solve the problem that in face recognition applications posture change of human face and external environment change such as illumination results in low recognition rate

and slow convergence speed and sparse representation used in face recognition

a face recognition algorithm based on multi-component Gabor feature extraction and adaptive weight selection collaborative representation（ GAW-CRC） is proposed. In feature extraction stage

the method eliminates component that has the weakest identify ability of all the Gabor transform feature components. The remaining components build feature dictionary

combining the feature component of corresponding test component

will have a final result by weighted merging together recognition results of all the remaining components according to the order of self-adaptive weighting function. Experiments show that this algorithm is possible to obtain a higher recognition rate in AR

FERET and Extended Yale B face database when the corresponding sample have changes in face angle

expression lighting conditions and other circumstances.