The invention discloses a living body human face detection method based on gray scale symbiosis matrixes and wavelet analysis. The method comprises: first of all, converting an RGB image comprising a human face area, which is obtained from a camera, into a gray scale image, compressing a gray scale grade to 16 grades, then respectively calculating four gray scale symbiosis matrixes (taking a distance of 1, and angles of 0 degree, 45 degrees, 90 degrees and 13 degrees respectively ), then extracting four texture characteristic quantities including energy, entropy, moment of inertia and correlation on the basis of the gray scale symbiosis matrixes, and respectively obtaining a mean value and a variance for the four texture characteristic quantities of the four gray scale symbiosis matrixes; at the same time, performing secondary decomposition on an original image by use of a Haar small wavelet base, extracting the coefficient matrixes of sub-bands HH1 and HH2 and obtaining a mean value and a variance; and finally sending all characteristic values as samples to be detected to a trained support vector machine for detection, and performing classification identification on real or counterfeit face images. The method provided by the invention has the advantages of reduced calculating complexity and improved detection accuracy.