In this study, photometric stereo imaging is used to reconstruct the stereo morphology of signature handwriting. Based on gray data quantitative analysis of the sample image, two characteristic indicators of the regional gray difference D and the gray average change trend V' of adjacent regions gray levels are proposed to examine the correlation between three-dimensional (3D) morphological images of the signature handwriting and the signer’s writing characteristics. In the experiment, 1000 signatures from five volunteers are collected to form 2600 single-character sample sets, and 500 copied signatures are collected to form 1300 single-character sample sets. Based on the Lib-SVM classifier, the Canny operator is used to extract edge features, and image processing, such as dilation and corrosion, is performed. The method locates the feature region and uses the LBP(Local Binary Pattern) operator to extract the texture information of the feature region as a feature vector for classification. The experimental results show that a binary classification model of the feature area processed using image processing technology can effectively differentiate the 3D morphology of the real and copied signature handwriting. The proposed method has a higher accuracy rate than the unprocessed sample in the characteristic area, proving that the method is scientific and feasible. It can provide technical support for signature-handwriting examination.