Face recognition using multimodal biometric features

This paper presents a new multimodal biometric approach using face and periocular biometric. The available face recognition algorithm performance in presence of multiple variations such as illumination, pose, expression, occlusion and plastic surgery is not satisfactory. Also, periocular biometrics face problems in presence of spectacles, head angle, hair and expression. A method which can extract multiple feature information from a single source and can give a satisfactory performance even with less number of training images is desirable. Thus combining face and periocular data obtained from the same image may increase the performance of the recognition system. A detailed performance analysis of face recognition and periocular biometric using Gabor and LBP features is carried out. This is then compared with the proposed multimodal biometric feature extraction technique. The experimental results obtained using Muct and plastic surgery face database shows that the proposed multimodal biometric performs better than other face recognition and individual biometric methods.