A Novel Illumination Estimation for Face Recognition under Complex Illumination Conditions

Abstract

SUMMARYAfter exploring the classic Lambertian reflectance model, we proposed an effective illumination estimation model to extract illumination invariants for face recognition under complex illumination conditions in this paper. The estimated illumination by our method not only meets the actual lighting conditions of facial images, but also conforms to the imaging principle. Experimental results on the combined Yale B database show that the proposed method can extract more robust illumination invariants, which improves face recognition rate.

Improvement of Illumination-Insensitive Feathers for Face Recognition under Complex Illumination Conditions

“…PCA [32], KFA [33] and LDA [34] are used for testing in the same conditions, because they are highly sensitive to illumination variation. MSR [10], DCT [11], Gradient [20], Weber [21] and Cheng [22], as the state-of-the-art methods, are selected to implement performance comparison.…”

“…The recognition rates of PCA, KFA and LDA based on our method are 95.62%, 97.78% and 98.72%, respectively. Specifically speaking, the recognition rate of performing the other approaches: MSR [10], DCT [11], Gradient [20], Weber [21] and Cheng [22] are discriminatively lower than performing our method on the Yale B+ dataset containing with full illumination variation. …”

“…Where mean(·) denotes the average value of an image and k∈[0, 1] is a parameter, which is set 0.6 according to [22]. For strong relationship among neighboring pixels of illuminations and better preservation of image edge information, anisotropic diffusion filter is proposed [24].…”

“…Gradient-based methods completely discard intensity information of facial reflectance. Cheng [22] proposes an illumination estimation algorithm to extract illumination invariants to meet the actual lighting situations and image acquisition model. In all, most of those methods based on the common hypothesis only extract high-frequency characteristics of illumination invariants (i.e., facial reflectance), neglect its low-frequency characteristic causing false edge phenomenon.…”

Improvement of illumination‐insensitive features for face recognition under complex illumination conditions

“…Gradient‐based methods completely discard intensity information of facial reflectance. Cheng [22] proposes an illumination estimation algorithm to extract illumination invariants to meet the actual lighting situations and image acquisition model. In all, most of those methods based on the common hypothesis only extract high‐frequency characteristics of illumination invariants (i.e.…”