Face illumination recovery for the deep learning feature under severe illumination variations.

• The illumination recovery model converts severe varying illumination to slight/moderate varying illumination for the deep learning feature. • The gradient descent algorithm is employed to tackle the illumination recovery model. • The GRI is generated by normalizing singular values of the logarithm version of the severe illumination variation face image to have unit L2-norm. • The GRIR preserves better face inherent information than the GRI. The deep learning feature is the best for face recognition nowadays, but its performance exhibits unsatisfactorily under severe illumination variations. The main reason is that the deep learning feature was trained by the internet face images with variations of large pose/expression and slight/moderate illumination, which cannot well tackle severe illumination variations. Inspired by the fact that the deep learning feature can cope well with slight/moderate varying illumination, this paper proposes an illumination recovery model to transform severe varying illumination to slight/moderate varying illumination. The illumination recovery model enables the illumination of the severe illumination variation image close to that of the reference image with slight/moderate varying illumination. The reference image generated from the severe illumination variation image is termed as the generated reference image (GRI), which is obtained by normalizing singular values of the logarithm version of the severe illumination variation image to have unit L2-norm. The gradient descent algorithm is employed to address the proposed illumination recovery model, to obtain the generated reference image based illumination recovery image (GRIR). GRIR preserves better face inherent information than GRI such as the face color. Experimental results indicate that the proposed GRIR can efficiently improve the performance of the deep learning feature under severe illumination variations. [ABSTRACT FROM AUTHOR]