TY - JOUR
T1 - Normalization of face illumination based on large-and small-scale features
AU - Xie, Xiaohua
AU - Zheng, Wei Shi
AU - Lai, Jianhuang
AU - YUEN, Pong Chi
AU - Suen, Ching Y.
N1 - Funding Information:
Manuscript received December 02, 2009; revised July 30, 2010; accepted November 14, 2010. Date of publication December 06, 2010; date of current version June 17, 2011. This work was supported in part by NSF-Guangdong under Grant U0835005, by the NSFC under Grant 60633030, by the 973 Program under Grant 2011CB302204, by the 985 Project at Sun Yat-sen Univeristy under Grant 35000-3181305, and by the NSERC under Grant N00009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Luminita Vese.
PY - 2011/7
Y1 - 2011/7
N2 - A face image can be represented by a combination of large-and small-scale features. It is well-known that the variations of illumination mainly affect the large-scale features (low-frequency components), and not so much the small-scale features. Therefore, in relevant existing methods only the small-scale features are extracted as illumination-invariant features for face recognition, while the large-scale intrinsic features are always ignored. In this paper, we argue that both large-and small-scale features of a face image are important for face restoration and recognition. Moreover, we suggest that illumination normalization should be performed mainly on the large-scale features of a face image rather than on the original face image. A novel method of normalizing both the Small-and Large-scale (S&L) features of a face image is proposed. In this method, a single face image is first decomposed into large-and small-scale features. After that, illumination normalization is mainly performed on the large-scale features, and only a minor correction is made on the small-scale features. Finally, a normalized face image is generated by combining the processed large-and small-scale features. In addition, an optional visual compensation step is suggested for improving the visual quality of the normalized image. Experiments on CMU-PIE, Extended Yale B, and FRGC 2.0 face databases show that by using the proposed method significantly better recognition performance and visual results can be obtained as compared to related state-of-the-art methods.
AB - A face image can be represented by a combination of large-and small-scale features. It is well-known that the variations of illumination mainly affect the large-scale features (low-frequency components), and not so much the small-scale features. Therefore, in relevant existing methods only the small-scale features are extracted as illumination-invariant features for face recognition, while the large-scale intrinsic features are always ignored. In this paper, we argue that both large-and small-scale features of a face image are important for face restoration and recognition. Moreover, we suggest that illumination normalization should be performed mainly on the large-scale features of a face image rather than on the original face image. A novel method of normalizing both the Small-and Large-scale (S&L) features of a face image is proposed. In this method, a single face image is first decomposed into large-and small-scale features. After that, illumination normalization is mainly performed on the large-scale features, and only a minor correction is made on the small-scale features. Finally, a normalized face image is generated by combining the processed large-and small-scale features. In addition, an optional visual compensation step is suggested for improving the visual quality of the normalized image. Experiments on CMU-PIE, Extended Yale B, and FRGC 2.0 face databases show that by using the proposed method significantly better recognition performance and visual results can be obtained as compared to related state-of-the-art methods.
KW - Face recognition
KW - illumination normalization
KW - visual compensation
UR - http://www.scopus.com/inward/record.url?scp=79959591217&partnerID=8YFLogxK
U2 - 10.1109/TIP.2010.2097270
DO - 10.1109/TIP.2010.2097270
M3 - Journal article
C2 - 21138805
AN - SCOPUS:79959591217
SN - 1057-7149
VL - 20
SP - 1807
EP - 1821
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 7
M1 - 5658185
ER -