Thermal face recognition using face localized scale-invariant feature transform

Abstract

Biometric face reorganization is an established means for the prevention of frauds in financial transactions and security issues. In particular, face verification has been extensively used to endorse financial transactions. Thermal face recognition is an upcoming approach in this field. This work proposes a robust thermal face recognition system based on face localized scale-invariant feature transform (FLSIFT). FLSIFT tackles the problem of thermal face recognition with complex backgrounds. Experimental results of proposed FLSIFT thermal face recognition system are compared with the existing Blood Vessel Pattern method. To test the performance of proposed and existing method, a new thermal face database consisting of Indian people and variations in the background is developed. The thermal facial images of 113 subjects are captured for this purpose. The test results show that the recognition accuracy of Blood Vessel Pattern technique and FLSIFT on face images with simple background is 79.28 % and 100 %, respectively. Moreover, the test performance on the complex background for the two methods is found to be 5.55 % and 98.14 %, respectively. It may be noted that FLSIFT is capable to handle background changes more efficiently and the performance is found to be robust.