Application of intelligent algorithm in face texture extraction
Wang Zihan, Sai Li, Chuantao Wang, li shuqi, yang mengru, Zhang tianyi · 2022
In this paper, 45 male faces and 45 female faces were collected to evaluate the texture of human face, gray level cooccurrence Matrix (GLCM) was used to extract the texture features of male and female faces, such as energy, contrast, correlation and entropy. T test was performed with SPSS17.0 software, and the results were compared and analyzed, the experimental results show that there are statistical differences between men and women in the four features. Based on the analysis of texture and pore depth, Pearson Correlation Coefficient is 0.9795, and the correlation between pore depth and texture is very strong.