Human recognition from multi angular images
R. Ramulu Naik, Kalpesh Lad · Kalpa publications in computing · 2018
Face recognition is still complicated task because to envision human actions might not realizable in each incident. Intention of face recognition is to identify human based on face that is similar from available dataset face images. Human face has multidimensional structure so it requires efficient technique for face harmonization and verification. Proposed work aim for developing efficient human face recognition method that deals with front as well as side view face in normal face expression. Using Viola–Jones face detection algorithm it accumulate only face region. Face features like eyes, nose and lip are extracted from whole face region using canny edge detection and harris corner detection method. To match individual face features, it compares position of edge boundary of features between images. Authors’ uses Euclidian distance method to retrieves maximum match value among all store face images. Based on threshold value it decides whether human face is recognized or not. Authors have evaluated performance of proposed method with DCT, DWD, PCA and LFL method on public free database like FEI, CVL and MIT-CBCL.