The invention discloses a human face recognition algorithm combining curvature and wavelet-contour enhancement. The method comprises: firstly enhancing the whole contour of a human face image by virtue of a level set curvature and a wavelet of an image, wherein the influence of illumination, expressions of human face and part of occluders in human face recognition is neglected; and then extracting human face characteristics by virtue of PCA algorithm. According to an existing method, the recognition rate and robustness are greatly reduced as illumination, expressions and occluders are different. In order to overcome the defect that the human face recognition rate is low under the non-binding changing conditions such as illumination, expressions, gestures and the like and improve the robustness of the recognition method, the invention provides the human face recognition algorithm combining curvature and wavelet-contour enhancement by using the advantages of the level set curvature and SRC. The algorithm takes full advantage of the property of the level set curvature, invariance of the human face image contour and the insensitivity of the human face image contour to illumination and improves the recognition rate of a non-binding human face and enhances the robustness of the recognition system based on an existing sparse representation theory.