Optimal fusion aided face recognition from visible and thermal face images

This paper proposes a novel Eigen face recognition that is aided by fusion of visible and thermal face images to improve the face recognition accuracy. We adopt three different fusion schemes where in the face information is fused by the optimal weights obtained by different optimization algorithms. The first two fusion approaches operate in the dual tree discrete wavelet transform (DT-DWT), while the third one operates in the Curvelet transform (CT) domain. We employ particle swarm optimization (PSO), self-tuning particle swarm optimization (STPSO) and brain storm optimization algorithm (BSO) to find optimal fusion coefficients. The proposed fusion aided face recognition approaches are evaluated through extensive experiments using OCTVBS benchmark face database and the Eigen face detection methodology. Simulation results show that proposed face recognition techniques have significant performance improvement in recognition accuracy suggesting fusion aided face recognition approach that deserves further study and consideration whenever high recognition accuracy is desired.