A Novel Feature Extraction Method for Texture and Shape Analysis of Face Makeup Database - Singh, Rohita; Raghuwanshi, Monika (International Journal of Computer Sciences and Engineering, 2019)
Human face images are very important for the identity of human faces and used for many applications such as authentication, or medical fields for analysis. The face retrieval and detection from the large database is a difficult problem. It becomes more challenging in the presence of makeup on the faces. Makeup is done in the different parts of the face such as lips, eyes, or on cheeks. Therefore, it is required to first detect the makeup on the image and then use efficient face recognition method. In this paper a novel texture and shape based feature extraction methods are presented using the wavelet based feature fusion for the efficient face recognition. The goal is to recognize the quarry face within the image database. The detection algorithm is very simple and fast to work for large databases. First a random quarry image is picked from database then features are extracted from both quarry and template images. Method first resizes the quarry and template images and then calculates features in RGB domain. For the texture analysis the Local Ternary Pattern (LTP) based feature are adopted in place of Local binary pattern (LBP). For feature enhancement the wavelet based fusion of lower and upper LTP patterns are proposed in the paper. Method is calculated and compared for images with and without makeup. To analyze the shape features Histogram of Gradient are plotted. The performance of our proposed feature extraction is tested using the Face images of man’s and women’s with heavy and light makeup and also without makeup.