title: A de-illumination scheme for face recognition based on fast decomposition and detail feature fusion.
abstract: Almost all the face recognition algorithms are unsatisfied due to illumination variation. Feature with high frequency represents the face intrinsic structure according to the common assumption that illumination varies slowly and the face intrinsic feature varies rapidly. In this paper, we will propose an adaptive scheme based on FBEEMD and detail feature fusion. FBEEMD is a fast version of BEEMD without time-consuming surface interpolation and iteration computation. It can decompose an image into sub-images with high frequency matching detail feature and sub-images with low frequency corresponding to contour feature. However, it is difficult to determine by quantitative analysis that which sub-images with high frequency can be used for reconstructing an illumination-invariant face. Thus, two measurements are proposed to calculate weights for quantifying the detail feature. With this fusion technique, one can reconstruct a more illumination-neutral facial image to improve face recognition rate. Verification experiments using classical recognition algorithms are tested with Yale B, PIE and FERET databases. The encouraging results show that the proposed scheme is very effective when dealing with face images under variable lighting condition.
authors.affiliation: State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai 200240, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhou
authors.forename: Sheng-Tong
authors.initials: ST
authors.lastname: Zhou
authors.forename: Zuo-Yang
authors.initials: ZY
authors.lastname: Zhong
authors.forename: Hong-Guang
authors.initials: HG
authors.lastname: Li
mesh_terms: D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008029:Lighting / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*