A review on Gabor wavelets for face recognition

Shen, Lianfeng; Li, Bai

doi:10.1007/s10044-006-0033-y

Cited by 414 publications

(187 citation statements)

References 65 publications

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“…For each patch or RFS region, the Gabor filter [54] is employed for feature extraction, which is formulated as…”

Section: Feature Extractionmentioning

confidence: 99%

Active AU Based Patch Weighting for Facial Expression Recognition

Xie

Yang

Lai

2017

Preprint

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Abstract:Facial expression has lots of applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on Action Unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. Sparse representation based approach is then proposed to detect the active AUs of testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for Jaffe and CK+ databases, respectively. Better cross-database performance has also been observed.

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“…For each patch or RFS region, the Gabor filter [54] is employed for feature extraction, which is formulated as…”

Section: Feature Extractionmentioning

confidence: 99%

Active AU Based Patch Weighting for Facial Expression Recognition

Xie

Yang

Lai

2017

Preprint

Self Cite

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“…The Gabor filters, which could effectively extract the image local directional features on multiple scales, have been successfully and prevalently used in FR [16][17] [18]. Very recently, Zhou et al [47] proposed to combine the perceptual features by Gabor filtering with diffusion distance for FR; Du et al [48] proposed to perform FR with non-uniform multilevel selection of Gabor features instead of the uniform down-sampling of Gabor features; a local Gabor based FR with improved accuracy by the selection of Gabor jets was presented in [49]; and multimodal FR using Gabor feature was presented in [50].…”

Section: Introductionmentioning

confidence: 99%

Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary

Yang

Zhang

Shiu

et al. 2013

Pattern Recognition

141

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By representing the input testing image as a sparse linear combination of the training samples via l 1 -norm minimization, sparse representation based classification (SRC) has shown promising results for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to code the occluded portions of face images, SRC could lead to robust FR results against face occlusion. However, the l 1 -norm minimization and the high number of atoms in the identity occlusion dictionary make the SRC scheme computationally very expensive. In this paper, a Gabor feature based robust representation and classification (GRRC) scheme is proposed for robust FR. The use of Gabor features not only increases the discrimination power of face representation, but also allows us to compute a compact Gabor occlusion dictionary which has much less atoms than the identity occlusion dictionary. Furthermore, we show that with Gabor feature transformation, l 2 -norm could take place the role of l 1 -norm to regularize the coding coefficients, which reduces significantly the computational cost in coding occluded face images. Our extensive experiments on benchmark face databases, which have variations of lighting, expression, pose and occlusion, demonstrated the high effectiveness and efficiency of the proposed GRRC method.

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“…Recent researches have shown that local feature based methods [16][17][18][ [43][44][45][46][47][48] [26] are very promising in object recognition, texture classification and uncontrolled FR. Gabor filters, which could effectively extract local directional features on multiple scales, have been successfully used in FR [17][18]. Compared to the holistic feature based approaches such as Eigenface [2] and FisherFace [3], Gabor filtering is less sensitive to image variations (e.g., illumination, expression).…”

Section: Introductionmentioning

confidence: 99%