Aiming at problem of low precision and slow running of cascade regression model in locating face alignment because of dependent shape initialization and high complexity

an improved cascade regression face alignment algorithm is proposed.Face shape is initialized by affine transformation parameter regression

and initial shape of the transformed shape is closer to the ground truth for improving model accuracy and convergence speed.Random ferns that follows local learning principle are constructed in local area of each feature point

and learn to obtain easily and highly sparse binary feature to improve speed of model. Global linear regression is used to compute binary feature and obtain shape increment to complete face alignment. Experimental simulation results show that average error of face alignment reduces 11 %

and average cost of time decreases 14 % compared with original algorithm on LFPW

HELEN and AFW datasets.