Fusing continuous spectral images for face recognition under indoor and outdoor illuminants
Novel image fusion approaches, including physics-based weighted fusion, illumination adjustment and rank-based decision level fusion, for spectral face images are proposed for improving face recognition performance compared to conventional images. A new multispectral imaging system is briefly presented which can acquire continuous spectral face images for our concept proof with fine spectral resolution in the visible spectrum. Several experiments are designed and validated by calculating the cumulative match characteristics of probe sets via the well-known recognition engine-FaceIt(A (R)). Experimental results demonstrate that proposed fusion methods outperform conventional images when gallery and probes are acquired under different illuminations and with different time lapses. In the case where probe images are acquired outdoors under different daylight situations, the fused images outperform conventional images by up to 78%.
face recognition image fusion multispectral imaging principle component analysis wavelet transform