Concatenation of Multiple Features for Face Recognition
Viswanath K. Reddy, Shruthi B. Gangal · Communications in computer and information science · 2016
Face recognition from surveillance camera is a challenging task due to variation in lighting conditions, motion blur and poses. Most of the face recognition algorithms perform well under controlled environments. In uncontrolled scenarios, face recognition algorithms are being developed to operate on information fused from multiple cameras. This approach increases the hardware and processing speed. In this paper effect of concatenating multiple features on the face recognition rate is being investigated. The developed algorithm is tested on the publicly available chokepoint dataset. Recognition rates achieved by concatenating multiple features are found to outperform the results of the methods using information from multiple cameras for face recognition. Further testing with various features need to be performed.