Linear Regression for Face Recognition

Naseem, Imran; Togneri, Roberto; Bennamoun, Mohammed

doi:10.1109/tpami.2010.128

Cited by 933 publications

(519 citation statements)

References 24 publications

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“…The performance of the proposed method has been compared with other advanced methods already reported in the literature. Some of the state-of-the-art methods reported in the literature are Linear Regression for Face Recognition [30], local derivative pattern (LDP) versus LBP [31], Ageinvariant face recognition [32] and local texture feature (LTF) [33]. From the comparative analysis, it has been found that our proposed method gives better recognition and rejection rate compared to other advanced methods.…”

Section: Database Methodsmentioning

confidence: 99%

Robust face recognition technique using Gabor phase pattern and phase only correlation

Arjun

Nair

2014

CSIT

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A robust and efficient method for face recognition using phase only correlation (POC) is proposed in this paper. To achieve efficient recognition rate, it uses the concept of histogram of Gabor phase pattern (HGPP) supplemented by POC technique. In HGPP, the quadrantbit codes are first extracted from faces, and in order to encode the phase variations, global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are derived. GGPP and LGPP are then split into the non-overlapping rectangular regions. From the above regions, spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. The recognition is carried out with the nearest-neighbor classifier, using the histogram intersection as the similarity measurement. Finally, face patterns are verified with POC based matching technique to improve the accuracy of the system. This method improves the result both distribution wise and content wise. Experiments are done on the large scale ORL, YALE, FERET and DCSKU databases. Experimental results show that the proposed method is promising and is comparable with the advanced face recognition algorithms reported in the literature.

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Section: Database Methodsmentioning

confidence: 99%

Robust face recognition technique using Gabor phase pattern and phase only correlation

Arjun

Nair

2014

CSIT

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“…It is observed from results that ČTROIKA and ŘTROIKA based classifier always performs better than their MLP counterparts. The authors in [23] addressed the facial expression variations and contiguous occlusion whereas the pose and illumination variations were not considered. The proposed work copes well with pose and illumination variations.…”

Section: Performance With Feret Face Datasetmentioning

confidence: 99%

Development of Hybrid Learning Machine with Complex Domain Neurocomputing

Srivastava¹,

Tripathi²

2018

IJIES

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This paper presents a hybrid learning machine for human identification which is an amalgamation of eigenface method, fisherface method, genetic fuzzy distribution and complex neural network. This hybrid machine constitutes multiple modules including the image representation in lower dimensional feature space, unsupervised clustering and supervised classification. The classification module employs proposed complex neuron structure ČTROIKA as hidden neurons, complex conventional neuron (ČMLP) as output neurons along with complex resilient propagation (ČRPROP) learning procedure. The efficacy and potency of the proposed classifier based hybrid learning machine demonstrated on two benchmark biometric datasets--FERET and AR face datasets which shows that our learning machine outperforms over state-of-the-art methods. Further, the classification module in the proposed learning machine is replaced by different classifiers to carry out the comparative analysis of various classifiers. The variations in classifier is based on the type of hidden neuron and learning algorithm. The performance comparisons of different classifiers reflects the superiority of the proposed classifier in terms of accuracy, speedy convergence, better learning with reduced learning cycles.

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“…We compare GRRC with SRC [10], CRC [32], Linear Regression for Classification (LRC) [37], linear Support Vector Machine (SVM) and Nearest Neighbor (NN) methods. If no specific instruction, for all the competing methods we use PCA to reduce the feature dimension.…”

Section: Face Recognition With Little Deformationmentioning

confidence: 99%

“…The manifold learning methods were proposed to overcome this limitation [5][6], and the representative manifold learning methods include locality preserving projection (LPP) [7], local discriminant embedding (LDE) [8], unsupervised discriminant projection (UDP) [9], etc. Besides, in order to better exploit the prior knowledge that face images from a single subject could construct a subspace, nearest subspace (NS) classifiers [19] [35][36] [37] are developed, which are usually superior to the simple nearest neighbor (NN) classifier.…”

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

142

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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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Linear Regression for Face Recognition

Cited by 933 publications

References 24 publications

Robust face recognition technique using Gabor phase pattern and phase only correlation

Robust face recognition technique using Gabor phase pattern and phase only correlation

Development of Hybrid Learning Machine with Complex Domain Neurocomputing

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

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