Close the loop: Joint blind image restoration and recognition with sparse representation prior (original) (raw)

Face Recognition in Low-Quality Images using Adaptive Sparse Representations

Although unconstrained face recognition has been widely studied over recent years, state-of-the-art algorithms still result in an unsatisfactory performance for low-quality images. In this paper, we make two contributions to this field: the first one is the release of a new dataset called 'AR-LQ' that can be used in conjunction with the well-known 'AR' dataset to evaluate face recognition algorithms on blurred and low-resolution face images. The proposed dataset contains five new blurred faces (at five different levels, from low to severe blurriness) and five new low-resolution images (at five different levels, from 66 ⇥ 48 to 7 ⇥ 5 pixels) for each of the hundred subjects of the 'AR' dataset. The new blurred images were acquired by using a DLSR camera with manual focus that takes an out-of-focus photograph of a monitor that displays a sharp face image. In the same way, the low-resolution images were acquired from the monitor by a DLSR at different distances. Thus, an attempt is made to acquire low-quality images that have been degraded by a real degradation process. Our second contribution is an extension of a known face recognition technique based on sparse representations (ASR) that takes into account low-resolution face images. The proposed method, called blur-ASR or bASR, was designed to recognize faces using dictionaries with different levels of blurriness. These were obtained by digitally blurring the training images, and a sharpness metric for matching blurriness between the query image and the dictionaries. These two main adjustments made the algorithm more robust with respect to low-quality images. In our experiments, bASR consistently outperforms other state-of-art methods including hand-crafted features, sparse representations, and a seven well-known deep learning face recognition techniques with and without super resolution techniques. On average, bASR obtained 88.8% of accuracy, whereas the rest obtained less than 78.4%.

A Hybrid Sparse Representation Model for Image Restoration

Sensors, 2022

Group-based sparse representation (GSR) uses image nonlocal self-similarity (NSS) prior to grouping similar image patches, and then performs sparse representation. However, the traditional GSR model restores the image by training degraded images, which leads to the inevitable over-fitting of the data in the training model, resulting in poor image restoration results. In this paper, we propose a new hybrid sparse representation model (HSR) for image restoration. The proposed HSR model is improved in two aspects. On the one hand, the proposed HSR model exploits the NSS priors of both degraded images and external image datasets, making the model complementary in feature space and the plane. On the other hand, we introduce a joint sparse representation model to make better use of local sparsity and NSS characteristics of the images. This joint model integrates the patch-based sparse representation (PSR) model and GSR model, while retaining the advantages of the GSR model and the PSR mod...

Sparse representation for face recognition: A review paper

IET Image Processing

With the increasing use of surveillance cameras, face recognition is being studied by many researchers for security purposes. Although high accuracy has been achieved for frontal faces, the existing methods have shown poor performance for occluded and corrupt images. Recently, sparse representation based classification (SRC) has shown the state-ofthe-art result in face recognition on corrupt and occluded face images. Several researchers have developed extended SRC methods in the last decade. This paper mainly focuses on SRC and its extended methods of face recognition. SRC methods have been compared on the basis of five issues of face recognition such as linear variation, non-linear variation, undersampled, pose variation, and low resolution. Detailed analysis of SRC methods for issues of face recognition have been discussed based on experimental results and execution time. Finally, the limitation of SRC methods have been listed to help the researchers to extend the work of existing methods to resolve the unsolved issues. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A Survey on Sparse Representation based Image Restoration

In recent field of engineering, digital images gaining popularity due to increasing requirement in many fields like satellite imaging, medical imaging, astronomical imaging, poor-quality family portraits etc. Therefore, the quality of images matters in such fields. There are many ways by which the quality of images can be improved. Image restoration is one of the emerging methodologies among various existing techniques. Image restoration is a process that deals with methods used to recover an original scene from degraded observations. The primary goal of the image restoration is the original image is recovered from degraded or blurred image. The main aim of this survey is to represent different methodologies of restoration that provide state-of-the-art results. The motivation of the literature originates from filter concept, iterative methods and sparse representations. The restoration methods of filter concepts are evaluated with the help of performance metrics SNR (signal-to-noise-ratio). These ideas can be used as a good reference in the research field of image restoration.

Sparse Representation and Face Recognition

International Journal of Image, Graphics and Signal Processing, 2018

Now a days application of sparse representation are widely spreading in many fields such as face recognition. For this usage, defining a dictionary and choosing a proper recovery algorithm plays an important role for the method accuracy. In this paper, two type of dictionaries based on input face images, the method named SRC, and input extracted features, the method named MKD-SRC, are constructed. SRC fails for partial face recognition whereas MKD-SRC overcomes the problem. Three extension of MKD-SRC are introduced and their performance for comparison are presented. For recommending proper recovery algorithm, in this paper, we focus on three greedy algorithms, called MP, OMP, CoSaMP and another called Homotopy. Three standard data sets named AR, Extended Yale-B and Essex University are used to asses which recovery algorithm has an efficient response for proposed methods. The preferred recovery algorithm was chosen based on achieved accuracy and run time.

Face recognition by sparse representation

Compressed Sensing

In this chapter, we present a comprehensive framework for tackling the classical problem of face recognition, based on theory and algorithms from sparse representation. Despite intense interest in the past several decades, traditional pattern recognition theory still stops short of providing a satisfactory solution capable of recognizing human faces in the presence of real-world nuisances such as occlusion and variabilities in pose and illumination. Our new approach, called sparse representation-based classification (SRC), is motivated by a very natural notion of sparsity, namely, one should always try to explain a query image using a small number of training images from a single subject category. This sparse representation is sought via 1-minimization. We show how this core idea can be generalized and extended to account for various physical variabilities encountered in face recognition. The end result of our investigation is a full-fledged practical system aimed at security and access control applications. The system is capable of accurately recognizing subjects out of a database of several hundred subjects with state-of-the-art accuracy.

Sparse Representation Based Face Recognition with Limited Labeled Samples

2013 2nd IAPR Asian Conference on Pattern Recognition, 2013

Sparse representations have emerged as a powerful approach for encoding images in a large class of machine recognition problems including face recognition. These methods rely on the use of an over-complete basis set for representing an image. This often assumes the availability of a large number of labeled training images, especially for high dimensional data. In many practical problems, the number of labeled training samples are very limited leading to significant degradations in classification performance. To address the problem of lack of training samples, we propose a semi-supervised algorithm that labels the unlabeled samples through a multi-stage label propagation combined with sparse representation. In this representation, each image is decomposed as a linear combination of its nearest basis images, which has the advantage of both locality and sparsity. Extensive experiments on publicly available face databases show that the results are significantly better compared to state-of-theart face recognition methods in semi-supervised setting and are on par with fully supervised techniques.

Robust Coarse-to-Fine Sparse Representation for Face Recognition

Lecture Notes in Computer Science, 2013

Recently Sparse Representation-based classification (SRC) has been successfully applied to pattern classification. In this paper, we present a robust Coarse-to-Fine Sparse Representation (CFSR) for face recognition. In the coarse coding phase, the test sample is represented as a linear combination of all the training samples. In the last phase, a number of "nearest neighbors" is determined to represent the test sample to perform classification. CFSR produces the sparseness through the coarse phase, and exploits the local data structure to perform classification in the fine phase. Moreover, this method can make a better classification decision by determining an individual dictionary for each test sample. Extensive experiments on benchmark face databases show that our method has competitive performance in face recognition compared with other state-of-the-art methods.

Sparse Representation for Color Image Restoration

IEEE Transactions on Image Processing, 2000

Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task , and shown to perform very well for various gray-scale image processing tasks. In this paper we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in . This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.

Multi-scale Sparse Representation for Robust Face Recognition

2011

Recently the Sparse Representation-based Classification (SRC) has been successfully used in face recognition. In SRC, a test image is coded by a linear combination of the training dictionary. In this paper, we propose a model extends from SRC named Multi-scale SRC (MSRC). The MSRC build the multi-scale dictionary for the training. A test image is then coded using this multi-scale dictionary. In addition, a voting scheme is applied which not only helps improving the recognition rate significantly, but also makes the algorithm more robust with occlusion. Experiments on representative face databases demonstrate that the MSRC is much more effective than the SRC.