The importance of the color information in face recognition

Torres, Luís; Reutter, J.Y.; Lorente, Luis Moreno

doi:10.1109/icip.1999.817191

Cited by 126 publications

(84 citation statements)

References 5 publications

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“…The common approach in most of the face recognition systems is to convert the color images to monochromatic form using simple transformations. There was also another approach where the RGB space has been compared with the YUV and HSV color models by L. Torres [8] for PCA based face recognition. Several studies have been made to evaluate color spaces.…”

Section: Introductionmentioning

confidence: 99%

Class Specific Threshold Selection in Face Space Using Set Estimation Technique for RGB Color Components

Datta

Murthy

2009

Lecture Notes in Computer Science

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Abstract. In conventional face recognition techniques, any query image is always classified to one of the face classes irrespective of whether the query image is a face or not. Most of the recognition algorithms are dissimilarity based, and one needs to put a proper threshold on the dissimilarity value for the sake of classification. In this paper, we have introduced a novel thresholding technique for classification, where the query image is not necessarily classified to one of the classes in the training set. The theoretical formulation of the thresholding technique and its utility are demonstrated on color face and non face datasets with RGB color components as features in the subspace. The proposed threshold selection is based on statistical method of set estimation and is guided by minimal spanning tree. Experiment shows that the proposed class specific threshold based technique performs better than the non threshold based systems using subspace algorithms.

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Section: Introductionmentioning

confidence: 99%

Class Specific Threshold Selection in Face Space Using Set Estimation Technique for RGB Color Components

Datta

Murthy

2009

Lecture Notes in Computer Science

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“…For face recognition, the eigenface analysis in the RGB color space domain is known not to be effective, because R, G, and B components are largely correlated with each other. Some literatures also pointed that the RGB domain is inadequate for face recognition (Torres et al, 1999). Instead of RGB color space, other color spaces that are less correlated between their components should be investigated for face recognition.…”

Section: Color Spaces For Face Recognitionmentioning

confidence: 99%

“…1(a). The final decision is made with the distances from three independent www.intechopen.com eigenface modules (Torres et al, 1999). Fig.…”

Section: Independent Color Face Recognitionmentioning

confidence: 99%

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Performance Comparison of Principal Component Analysis-Based Face Recognition in Color Space

Yoo¹,

Sim²,

Kim³

et al. 2011

Advanced Biometric Technologies

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“…Traditionally, face recognition systems have operated in the two-dimensional (21)) space. Most systems use digitised gray-scale photographs of faces (intensity images), though some research involving colour images is also found in literature (Torres et al 1999). Intensity images measure the amount of light reflecting from a surface and hitting a photosensitive device (camera).…”

Section: Automated Face Recognitionmentioning

confidence: 99%

Automatic Face Recognition Using Stereo Images

Samani

Winkler

Niranjan

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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Face recognition is an important pattern recognition problem, in the study of both natural and artificial learning problems. Compaxed to other biometrics, it is non-intrusive, noninvasive and requires no paxticipation from the subjects. As a result, it has many applications varying from human-computer-interaction to access control and law-enforcement to crowd surveillance.In typical optical image based face recognition systems, the systematic vaxiability arising from representing the three-dimensional (3D) shape of a face by a two-dimensional (21)) illumination intensity matrix is treated as random vaxiability. Multiple examples of the face displaying vaxying pose and expressions axe captured in different imaging conditions. The imaging environment, pose and expressions are strictly controlled and the images undergo rigorous normalisation and pre-processing. This may be implemented in a paxtially or a fully automated system. Although these systems report high classification accuracies (>90%), they lack versatility and tend to fail when deployed outside laboratory conditions.Recently, more sophisticated 3D face recognition systems haxnessing the depth information have emerged. These systems usually employ specialist equipment such as laser scanners and structured light projectors. Although more accurate than 2D optical image based recognition, these systems are equally difficult to implement in a non-co-operative environment.Existing face recognition systems, both 2D and 3D, detract from the main advantages of face recognition and fail to fully exploit its non-intrusive capacity. This is either because they rely too much on subject co-operation, which is not always available, or because they cannot cope with noisy data. The main objective of this work was to investigate the role of depth information in face recognition in a noisy environment. A stereo-based system, inspired by the human binocular vision, was devised using a pair of manually calibrated digital off-the-shelf cameras in a stereo setup to compute depth information. Depth values extracted from 2D intensity images using stereoscopy are extremely noisy, and as a result this approach for face recognition is rare. This was cofirmed by the results of our experimental work. Noise in the set of correspondences, camera calibration and triangulation led to inaccurate depth reconstruction, which in turn led to poor classifier accuracy for both 3D surface matching and 211) depth maps. Psychology literature elucidated that although depth information is crucial to the way humans recognise faces, this information is perceived through disparity information rather than actual depth in the form of familiar structure and texture information seen in 2D images. Hence, dispaxity information can be thought of as neural proxy for depth. Computationally too, depth and disparity are proportional up to camera parameters. Disparity values of sub-pixel accuracy are computed by matching the stereo image pairs to solve the ill-posed Correspondence Problem.A 211) image rep...

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The importance of the color information in face recognition

Cited by 126 publications

References 5 publications

Class Specific Threshold Selection in Face Space Using Set Estimation Technique for RGB Color Components

Class Specific Threshold Selection in Face Space Using Set Estimation Technique for RGB Color Components

Performance Comparison of Principal Component Analysis-Based Face Recognition in Color Space

Automatic Face Recognition Using Stereo Images

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