Aiming at problem that traditional algorithm is inefficient in large shape variations and different face detectors

a face alignment algorithm based on improved local binary features is proposed.Different from traditional shape-indexed features

a relative-indexed features under a spatial interdependency assumption is designed.Moreover

this algorithm abandon a global linear learning and use a semi-global linear learning under this spatial interdependency assumption. A face alignment sensitivity analysis is used to compare robustness the algorithm under different face detector. Extensive experiments on 300-W benchmark dataset demonstrate that this algorithm outperforms the traditional cascaded regression algorithm.