PCA-based face recognition from video using super-resolution

Al-Azzeh, M.; Eleyan, Alaa; Demirel, Hasan

doi:10.1109/iscis.2008.4717885

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Zhou and Bhanu applied the IBP super-resolution technique to recover lost curvature details from low resolution face profile videos. 56 In a similar fashion, Al-Azzeh et al 54 combine this super-resolution scheme with an efficient frequency based alignment procedure that minimizes the warping error between the observations with respect to phase shift and image plane orientation. The superresolution algorithm drove a principal component analysis (PCA) based matcher to reach a 97% recognition rate in an experiment involving 50 video clips from 50 subjects, which marks an improvement over the 89% recognition rate achieved by the same matcher on native resolution images.…”

Section: Super-resolution Methodsmentioning

confidence: 99%

“…Face recognition systems typically perform dimensionality reduction techniques such as PCA, but in super-resolution schemes like the one employed in Ref. 54, the high resolution images are obtained from the low resolution frames prior to dimensionality reduction. Computational efficiency gains can be achieved by reversing the order of these processes.…”

Section: Super-resolution Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Face Recognition From Video: A Review

Barr

Bowyer

Flynn

et al. 2012

Int. J. Patt. Recogn. Artif. Intell.

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Driven by key law enforcement and commercial applications, research on face recognition from video sources has intensified in recent years. The ensuing results have demonstrated that videos possess unique properties that allow both humans and automated systems to perform recognition accurately in difficult viewing conditions. However, significant research challenges remain as most video-based applications do not allow for controlled recordings. In this survey, we categorize the research in this area and present a broad and deep review of recently proposed methods for overcoming the difficulties encountered in unconstrained settings. We also draw connections between the ways in which humans and current algorithms recognize faces. An overview of the most popular and difficult publicly available face video databases is provided to complement these discussions. Finally, we cover key research challenges and opportunities that lie ahead for the field as a whole.

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Section: Super-resolution Methodsmentioning

confidence: 99%

Section: Super-resolution Methodsmentioning

confidence: 99%

Face Recognition From Video: A Review

Barr

Bowyer

Flynn

et al. 2012

Int. J. Patt. Recogn. Artif. Intell.

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show abstract

“…The performance has been evaluated for AR face database and reported (95%) recognition rate. Al-Azzeh [18] proposed method uses super resolution to generate a super resolved video sequences from a low-resolution video sequences and uses frames acquired from the high-resolution video sequences to train and test the performance of the principal component analysis based face recognition system. Simultaneous super resolution and feature extraction for face recognition was proposed by Hennings-Yeomans [19], [20] to estimate a super resolution image by searching for similar features of the LR image in the training set.…”

Section: Existing Workmentioning

confidence: 99%

Enhancing face recognition at a distance using super resolution

Al-Hassan

Jassim

Sellahewa

2012

Proceedings of the on Multimedia and Security

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The characteristics of surveillance video generally include lowresolution images and blurred images. Decreases in image resolution leads to loss of high frequency facial components, which is expected to adversely affect recognition rates. Super resolution (SR) is a technique used to generate a higher resolution image from a given low-resolution, degraded image. In this paper, we investigate two existing super-resolution methods to reconstruct a high resolution from a single/ multiple lowresolution images. The first method is based on pairs of high and low quality dictionaries in the spatial domain and the other method is the so called Back-Project Iterative Interpolation for super resolution. Recent works have used SR as a pre-processing step to overcome the problem of low-resolution images in face recognition. However, super resolution reconstruction process being ill-posed results in visual artifacts that can be visually distracting to humans and/or effect machine feature extraction and face recognition algorithms. In this paper, we propose an alternative super resolution scheme based on dictionaries in high frequency wavelet subbands. The proposed method is evaluated on database of high and low-resolution images-Extended Yale B database and UBHSD database-and for face recognition at a distance. We shall demonstrate that the proposed approach at level 3 DWT resolution has superior performance in comparison to the other methods.

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Super-Resolution Method for Face Recognition Using Nonlinear Mappings on Coherent Features

Huang

2011

IEEE Trans. Neural Netw.

117

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Low-resolution (LR) of face images significantly decreases the performance of face recognition. To address this problem, we present a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analysis (PCA) based features of high-resolution (HR) and LR face images. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the PCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Facial Recognition Technology, University of Manchester Institute of Science and Technology, and Olivetti Research Laboratory databases show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.

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PCA-based face recognition from video using super-resolution

Cited by 3 publications

References 7 publications

Face Recognition From Video: A Review

Face Recognition From Video: A Review

Enhancing face recognition at a distance using super resolution

Super-Resolution Method for Face Recognition Using Nonlinear Mappings on Coherent Features

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