IEEE Trans Image Process - Normalization of face illumination based on large-and small-scale features.

Tópicos

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Resumo

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.

Resumo Limpo

face imag can repres combin largeand smallscal featur wellknown variat illumin main affect largescal featur lowfrequ compon much smallscal featur therefor relev exist method smallscal featur extract illuminationinvari featur face recognit largescal intrins featur alway ignor paper argu largeand smallscal featur face imag import face restor recognit moreov suggest illumin normal perform main largescal featur face imag rather origin face imag novel method normal smalland largescal sl featur face imag propos method singl face imag first decompos largeand smallscal featur illumin normal main perform largescal featur minor correct made smallscal featur final normal face imag generat combin process largeand smallscal featur addit option visual compens step suggest improv visual qualiti normal imag experi cmupi extend yale b frgc face databas show use propos method signific better recognit perform visual result can obtain compar relat stateoftheart method

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