An ingenious face recognition system based on HRPSM_CNN under unrestrained environmental condition

Abstract

Face recognition is an emerging technology that divulges various applications in diverse fields like medical image analysis, surveillance, personal identification, and security related cases. In order to effectively recognize the images from the known data sets, there are a number of face recognition algorithms which are in practice. However, a few problems are encountered in effective recognition with a satisfied parameter. Even though there are various algorithms like Local Binary pattern(LBP), Directional Binary Code(DBC), Multi Support Vector Machine(Multi- SVM), and Convolutional Neural Network(CNN)which are being used for face recognition, still the face recognition is not achieved satisfactorily especially for the large databases as the images are affected due to poor lighting and also owing to occlusion occurring in the stagnant pictures. Hence, a new approach called Hybrid Robust Point Set Matching Convolutional Neural Network(HRPSM_CNN) is proposed to effectively recognize the faces from the data sets over the unconstrained situations. This proposed method shows enhanced receiver operating characteristics when compared to the traditional algorithms. This HRPSM_CNN provides 97 % of accuracy rate for ORL and AR database and 96 % for LFW face database which are significantly higher than the existing traditional algorithms. The proposed algorithm is implemented in visually impaired assistive device and the results show better recognition under difficult situations like various lighting and weather conditions.

Keywords

• Face recognition
• LBP
• DBC
• Multi- SVM
• HRPSM_CNN