Image Quality Assessments and Restoration for Face Detection and Recognition System Images

Zamani, A.N.; Awang, M.K.; Omar, Naaman; Nazeer, Shahrin Azuan

doi:10.1109/ams.2008.168

Cited by 14 publications

(9 citation statements)

References 9 publications

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“…without any reference images, as the method in [32], or learnt knowledge from previous data sets such as in [33]; (ii) it is content-independent, i.e. the value of image quality can still reflect well the blur level of different frames while the content in the video changes, except for some extreme cases; (iii) it works with unknown blur model, i.e.…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

“…Esparragon et al [6] implemented an automatic quality measurement tool, which included these functions. Although there may be various quality problems with images, such as shadows, hot spots, video artefacts, blurring, distortions, blockiness, random noise and movement [8,15,32], we are only concerned here with image blurring, since this is the artefact that predominantly affects the result of super-resolution. Blur also influences the automation, robustness and efficiency of many visual systems, like systems for visual surveillance and 3D reconstruction.…”

Section: State Of the Artmentioning

confidence: 99%

“…[7]) and PSNR (e.g. [32]), while methods used to improve the image are blind deconvolution (e.g. [5,13,29]) and image restoration.…”

Section: State Of the Artmentioning

confidence: 99%

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Super-resolution in practice: the complete pipeline from image capture to super-resolved subimage creation using a novel frame selection method

Petrou

Jaward

Chen

et al. 2011

Machine Vision and Applications

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We present a complete super-resolution system using a camera, that is assumed to be on a vibrating platform and continually capturing frames of a static scene, that have to be super-resolved in particular regions of interest. In a practical system the shutter of the camera is not synchronised with the vibrations it is subjected to. So, we propose a novel method for frame selection according to their degree of blurring and we combine a tracker with the sequence of selected frames to identify the subimages containing the region of interest. The extracted subimages are subsequently co-registered using a state of the art sub-pixel registration algorithm. Further selection of the co-registered subimages takes place, according to the confidence in the registration result. Finally, the subimage of interest is super-resolved using a state of the art super-resolution algorithm. The proposed frame selection method is of generic applicability and it is validated with the help of manual frame quality assessment.

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Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

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Super-resolution in practice: the complete pipeline from image capture to super-resolved subimage creation using a novel frame selection method

Petrou

Jaward

Chen

et al. 2011

Machine Vision and Applications

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“…While factors such as illumination, pose variation, blur and focus change may drastically deteriorate recognition precision [1], thus it is necessary to take these factors into account to automatically evaluate quality of facial images. For general IQA, there are several most popular full-reference schemes such as the Mean Squared Error (MSE) [2], Peak Signal-to-Noise-Ratio (PSNR) [3], the SSIM index [4] and Tone Rendering Distortion Index (TRDI) [5], but they all need reference images which are hard to obtain in practical application.…”

Section: Introductionmentioning

confidence: 99%

Fusion of mSSIM and SVM for Reduced-Reference Facial Image Quality Assessment

Fang

Zhou

et al. 2012

Biometric Recognition

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Abstract. Image Quality Assessment (IQA) is a critical part in face recognition system for helping to pick out the better quality images to assure high accuracy. In this paper, we propose a simple but efficient facial IQA algorithm based on Bayesian fusion of modified Structural Similarity (mSSIM) index and Support Vector Machine (SVM) as a reduced-reference method for facial IQA. The fusion scheme largely improves the facial IQA and consequently promotes the precision of face recognition when comparing to mSSIM or SVM alone. Experimental validation shows that the proposed algorithm works well in multiple feature spaces on many face databases.

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“…One such issue is that facial recognition tools are in general not efficient for poor quality facial images, e.g. in the presence of shadows, artifacts, and blurring (Zamani et al, 2008;Zhao et al, 2003;Castillo, 2006).…”

Section: Introductionmentioning

confidence: 99%

Low-cost Automatic Inpainting for Artifact Suppression in Facial Images

Sobiecki¹,

Telea²,

Giraldi³

et al. 2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:Facial images are often used in applications that need to recognize or identify persons. Many existing facial recognition tools have limitations with respect to facial image quality attributes such as resolution, face position, and artifacts present in the image. In this paper we describe a new low-cost framework for preprocessing low-quality facial images in order to render them suitable for automatic recognition. For this, we first detect artifacts based on the statistical difference between the target image and a set of pre-processed images in the database. Next, we eliminate artifacts by an inpainting method which combines information from the target image and similar images in our database. Our method has low computational cost and is simple to implement, which makes it attractive for usage in low-budget environments. We illustrate our method on several images taken from public surveillance databases, and compare our results with existing inpainting techniques.

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Image Quality Assessments and Restoration for Face Detection and Recognition System Images

Cited by 14 publications

References 9 publications

Super-resolution in practice: the complete pipeline from image capture to super-resolved subimage creation using a novel frame selection method

Super-resolution in practice: the complete pipeline from image capture to super-resolved subimage creation using a novel frame selection method

Fusion of mSSIM and SVM for Reduced-Reference Facial Image Quality Assessment

Low-cost Automatic Inpainting for Artifact Suppression in Facial Images

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