Facial deblur inference to improve recognition of blurred faces

Nishiyama, Masashi; Takeshima, Hidenori; Shotton, Jamie; Kozakaya, Tatsuo; Yamaguchi, Osamu

doi:10.1109/cvpr.2009.5206750

Cited by 21 publications

(19 citation statements)

References 28 publications

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“…Very recently, Nishiyama et al [5] introduced an approach exploiting face prior information for facial deblurring, yielding in very promising results. The idea is to exploit prior knowledge of how facial appearances are changed by blur.…”

Section: Facial Deblurringmentioning

confidence: 98%

“…We first analyzed synthesized images by blurring sharp query faces from FERET database as in [5]. Sharp faces are blurred by Gaussian PSFs (σ = 2,4,6,8).…”

Section: Experimental Analysismentioning

confidence: 99%

“…Section 2 briefly describes a recent approach using facial deblurring [5] while Section 3 discusses another promising technique based on blur-invariant descriptors [2]. These two methods can be considered as stateof-the-art in recognition of blurred faces.…”

Section: Introductionmentioning

confidence: 99%

“…Blur is unfortunately often present in real-world face images and is usually originated from camera motion or misfocused optics. It affects the appearance of faces in images, causing two main problems for face recognition: (i) the facial appearance of an individual changes drastically due to blur; (ii) and different individuals tend to appear more similar when blurred [5].…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Blurred Faces via Facial Deblurring Combined with Blur-Tolerant Descriptors

Hadid

Nishiyama

Sato

2010

2010 20th International Conference on Pattern Recognition

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Blur is often present in real-world images and significantly affects the performance of face recognition systems. To improve the recognition of blurred faces, we propose a new approach which inherits the advantages of two recent methods. The idea consists of first reducing the amount of blur in the images via deblurring and then extracting blur-tolerant descriptors for recognition. We assess our analysis on real blurred face images (FRGC 1.0 database) and also on face images artificially degraded by focus blur (FERET database), demonstrating significant performance enhancement compared to the state-of-the-art.

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Section: Facial Deblurringmentioning

confidence: 98%

“…We first analyzed synthesized images by blurring sharp query faces from FERET database as in [5]. Sharp faces are blurred by Gaussian PSFs (σ = 2,4,6,8).…”

Section: Experimental Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recognition of Blurred Faces via Facial Deblurring Combined with Blur-Tolerant Descriptors

Hadid

Nishiyama

Sato

2010

2010 20th International Conference on Pattern Recognition

Self Cite

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“…Face De-blurring. Despite significant advances in image and video de-blurring [54,72,51,50,24,3,14], de-blurring heavily blurred images is still an open problem. In this paper, some designs that use optical defocus for privacy may be susceptible to reverse engineering.…”

Section: Related Workmentioning

confidence: 99%

Privacy preserving optics for miniature vision sensors

Pittaluga

Koppal

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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The next wave of micro and nano devices will create a world with trillions of small networked cameras. This will lead to increased concerns about privacy and security. Most privacy preserving algorithms for computer vision are applied after image/video data has been captured. We propose to use privacy preserving optics that filter or block sensitive information directly from the incident lightfield before sensor measurements are made, adding a new layer of privacy. In addition to balancing the privacy and utility of the captured data, we address trade-offs unique to miniature vision sensors, such as achieving high-quality field-of-view and resolution within the constraints of mass and volume. Our privacy preserving optics enable applications such as depth sensing, full-body motion tracking, people counting, blob detection and privacy preserving face recognition. While we demonstrate applications on macroscale devices (smartphones, webcams, etc.) our theory has impact for smaller devices.

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Face Swapping for Solving Collateral Privacy Issues in Multimedia Analytics

Bailer

2018

MultiMedia Modeling

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Facial deblur inference to improve recognition of blurred faces

Cited by 21 publications

References 28 publications

Recognition of Blurred Faces via Facial Deblurring Combined with Blur-Tolerant Descriptors

Recognition of Blurred Faces via Facial Deblurring Combined with Blur-Tolerant Descriptors

Privacy preserving optics for miniature vision sensors

Face Swapping for Solving Collateral Privacy Issues in Multimedia Analytics

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