Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces

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

doi:10.1109/tpami.2010.203

Cited by 79 publications

(51 citation statements)

References 29 publications

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“…Motion blur, however, occurs when exposure time is not brief enough due to the rapid object moving or camera shaking. There are two main categories of approaches for improving the quality of the blurred face images: 1) blurred image modelling using methods such as subspace analysis [55] or sparse representation [82], and 2) blur-tolerant descriptor based methods which attempt to extract blur insensitive features such as Local Phase Quantization (LPQ) [1], [25].…”

Section: Face Recognition Algorithmsmentioning

confidence: 99%

People Identification and Tracking Through Fusion of Facial and Gait Features

Wei

et al. 2014

Biometric Authentication

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Abstract. This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance.

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Section: Face Recognition Algorithmsmentioning

confidence: 99%

People Identification and Tracking Through Fusion of Facial and Gait Features

Wei

et al. 2014

Biometric Authentication

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“…Work in the recognition of blurred faces [33] is also related to our method. Their approach extracts features from motion-blurred images of faces and then uses the subspace distance to identify the blur.…”

Section: Blurred Face Recognitionmentioning

confidence: 99%

Single Image Blind Deconvolution with Higher-Order Texture Statistics

Martinello

Favaro

2011

Video Processing and Computational Video

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Abstract. We present a novel method for solving blind deconvolution, i.e., the task of recovering a sharp image given a blurry one. We focus on blurry images obtained from a coded aperture camera, where both the camera and the scene are static, and allow blur to vary across the image domain. As most methods for blind deconvolution, we solve the problem in two steps: First, we estimate the coded blur scale at each pixel; second, we deconvolve the blurry image given the estimated blur. Our approach is to use linear high-order priors for texture and second-order priors for the blur scale map, i.e., constraints involving two pixels at a time. We show that by incorporating the texture priors in a least-squares energy minimization we can transform the initial blind deconvolution task in a simpler optimization problem. One of the striking features of the simplified optimization problem is that the parameters that define the functional can be learned offline directly from natural images via singular value decomposition. We also show a geometrical interpretation of image blurring and explain our method from this viewpoint. In doing so we devise a novel technique to design optimally coded apertures. Finally, our coded blur identification results in computing convolutions, rather than deconvolutions, which are stable operations. We will demonstrate in several experiments that this additional stability allows the method to deal with large blur. We also compare our method to existing algorithms in the literature and show that we achieve state-of-the-art performance with both synthetic and real data.

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“…The performances of face recognition could suffer from blur for the fact that blur leads to two main problems [6]:…”

Section: Introductionmentioning

confidence: 99%

“…A PSF is inferred through total variation regularization [16], the variation of Gaussian scale in the edges [17][18] [19], or variation in the wavelet domain [20] [21]. Recently, M. Nishiyama [6] has revealed that deblurring from a single image is an ill-posed problem and these deblurring methods are insufficient for accurate face recognition. Other methods deduce a PSF using multiple images [22][23], and M. Nishiyama et.al [6] [24] propose to build multiple PSFs and use the best match as the final PSF.…”

Section: Introductionmentioning

confidence: 99%

Blur-Robust Face Recognition via Transformation Learning

Zhang

et al. 2015

Lecture Notes in Computer Science

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Abstract. This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred images to a same feature subspace by the method of multidimensional scaling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the manifold structure of the original shape images and, at the same time, enhances the class separability, resulting in a wide applications to various descriptors. Furthermore, we combine our method with subspace-based point spread function (PSF) estimation method to handle cases of unknown blur degree, by applying the feature transformation corresponding to the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance against the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.

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Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces

Cited by 79 publications

References 29 publications

People Identification and Tracking Through Fusion of Facial and Gait Features

People Identification and Tracking Through Fusion of Facial and Gait Features

Single Image Blind Deconvolution with Higher-Order Texture Statistics

Blur-Robust Face Recognition via Transformation Learning

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