Quality assessment based denoising to improve face recognition performance
20 Jun 2011-pp 140-145
TL;DR: In the proposed framework, the optimal parameter set is selected for denoising using quality assessment algorithms with low complexity using quality score based parameter selection on the AR face dataset.
Abstract: A probe face image may contain noise due to environmental conditions, incorrect use of sensors or transmission error. The performance of face recognition severely depletes when the probe image is contaminated with noise. Denoising techniques can improve recognition performance, provided the correct parameters are used. In this paper, a parameter selection framework is presented. In the proposed framework, the optimal parameter set is selected for denoising using quality assessment algorithms with low complexity. Quality score based parameter selection is evaluated on the AR face dataset. A correlation study is discussed to ascertain the relationship between the quality scores and recognition rate. The experiments suggest that the proposed framework improves the performance both in terms of accuracy and computation time.
Citations
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TL;DR: The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.
Abstract: Biometric systems encounter variability in data that influence capture, treatment, and u-sage of a biometric sample. It is imperative to first analyze the data and incorporate this understanding within the recognition system, making assessment of biometric quality an important aspect of biometrics. Though several interpretations and definitions of quality exist, sometimes of a conflicting nature, a holistic definition of quality is indistinct. This paper presents a survey of different concepts and interpretations of biometric quality so that a clear picture of the current state and future directions can be presented. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in fingerprint, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images, to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems. The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.
119 citations
Cites background from "Quality assessment based denoising ..."
...Image restoration techniques can improve image quality, provided that the correct parameters are used [17]....
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...based on [17], and (b) quality-based multiclassifier selection, proposed in [26]....
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TL;DR: Face recognition has become the future development direction and has many potential application prospects and is introduced in the general evaluation standards and the general databases of face recognition.
Abstract: Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. People collect the face images, and the recognition equipment automatically processes the images. The paper introduces the related researches of face recognition from different perspectives. The paper describes the development stages and the related technologies of face recognition. We introduce the research of face recognition for real conditions, and we introduce the general evaluation standards and the general databases of face recognition. We give a forward-looking view of face recognition. Face recognition has become the future development direction and has many potential application prospects.
114 citations
Cites background from "Quality assessment based denoising ..."
...In the future, there may be a special camera for face recognition, which can improve the image quality and solve the problems of image filtering, image reconstruction [117], [118], denoising [119]–[121] etc....
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01 Apr 2013
TL;DR: Performance comparison between RFRS and other face recognition techniques shows that for most of the cases, RFRS performs better than other conventional techniques under different types of noises and it shows the high robustness of the proposed algorithm.
Abstract: Facial detection and recognition are among the most heavily researched fields of computer vision and image processing. Most of the current face recognition techniques suffer when the noises affect the global features or the local intensity pixels of the images under consideration. In the proposed Reliable Face Recognition System RFRS system, for the first time, a combination of Gabor Filter GF, Principal component analysis PCA for efficient feature extraction, and ANN for classification is employed. This demonstrates how to detect faces in noisy images by training the network several times on various input; ideal and noisy images of faces. Applying GF before PCA reduces PCA sensitivity to noise, provides a greater level of invariance, and trains the ANN on different sets of noisy images. The output of the ANN is a vector whose length equal to the distinct subjects in Olivetti Research Laboratory ORL. The ANN is trained to output a 1 in the correct position of the output vector and to fill the rest of the output vector with 0's. Experimentation is carried out on RFRS by using ORL datasets. The experimental results show that training the network on noisy input images of face greatly reduce its errors when it had to classify or recognize noisy images. For noisy face images, the network did not make any errors for faces with noise of mean 0.00 or 0.05, while the average recognition rate varies from 96.8% to 98%. When noise of mean 0.10 is added to the images the network begins to make errors. For noiseless face images, the proposed system achieves correct classification. Performance comparison between RFRS and other face recognition techniques shows that for most of the cases, RFRS performs better than other conventional techniques under different types of noises and it shows the high robustness of the proposed algorithm.
12 citations
TL;DR: Zhang et al. as discussed by the authors analyzed the effects of face image denoising and enhancement methods on the face recognition performance of the MXNet system architecture based face recognition system and showed that face image quality has a significant effect on the recognition performance.
Abstract: Face recognition technology has become an important quantitative examination method in the field of forensic identification of human images. However, face image quality affects the recognition performance of face recognition systems. Existing research on the effects of face image denoising and enhancement methods on the face recognition performance are typically based on facial images with manually synthesized noises rather than the noises under natural environmental corruption, and their studied face recognition techniques are limited on the traditional face recognition algorithms rather than state-of-the-art convolutional neural network based face recognition methods. In this work, face image materials from 33 real cases in forensic identification of human images were collected for quantitative analysis of the effects of face image denoising and enhancement methods on the deep face recognition performance of the MXNet system architecture based face recognition system. The results show that face image quality has a significant effect on the recognition performance of the face recognition system, and the image processing techniques can enhance the quality of face images, and then improve the recognition precision of the face recognition system. In addition, the effects of the Gaussian filtering are better than the self-snake model based image enhancement method, which indicates that the image denoising methods are more suitable for performance improvement of the deep face recognition system rather than the image enhancement techniques under the application of the practical cases.
10 citations
01 Jan 2013
TL;DR: A thorough analysis on the influence of different denoising techniques on the performance of various holistic 3D face recognition techniques is provided and results show that denoised can be applied using parameters significantly higher than those traditionally used as this improves the recognition performance.
Abstract: Although 3D face imaging is increasingly popular, many 3D facial imaging systems have significant noise components which needs to be reduced by post-processing if meaningful recognition results are desired. For best results, the denoising algorithm must be chosen appropriately, using the noise distribution, and its parameters tuned. In this paper, a thorough analysis on the influence of different denoising techniques on the performance of various holistic 3D face recognition techniques is provided. After resampling and denoising the input data, the face is cropped, aligned and normalised. This results in the feature space which is split into two sets: gallery and query. Recognition ranks are then computed on the feature space for different denoising algorithms. Result show that, despite its simplicity, the median filter produces the best results. However, its best results are achieved using a mask size much larger than is commonly used in 3D facial denoising. For all techniques, results show that denoising can be applied using parameters significantly higher than those traditionally used as this improves the recognition performance.
8 citations
Cites methods from "Quality assessment based denoising ..."
...For instance, in [2] and [14], automatic methods are provided to measure the quality of the 2D images and 3D depth maps of faces, respectively....
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References
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TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.
40,609 citations
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Abstract: Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.
14,245 citations
"Quality assessment based denoising ..." refers background or methods in this paper
...ture algorithms such as Local Binary Patterns (LBP) [7], are known to be more resilient towards these covariates compared to appearance based algorithms such as Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA)....
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...The experiment performed using data-driven noise and LBP demonstrates the effect of noise on face recognition....
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...The recognition accuracy is also computed for each set using LBP based face recognition algorithm [7]....
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...∙ The class label corresponding to each of the quality vector is the parameter 𝑃1..𝑖 which results in the best rank-1 efficiency with the training-gallery-set using local binary pattern (LBP) as the face recognition algorithm....
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...Tex- ture algorithms such as Local Binary Patterns (LBP) [7], are known to be more resilient towards these covariates compared to appearance based algorithms such as Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA)....
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01 Jan 1998
3,650 citations
"Quality assessment based denoising ..." refers methods in this paper
...The experiments are conducted on the AR face database [5] containing 756 frontal face images pertaining to 126 subjects (i....
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...As discussed in Section 4, SVM model is learned using the training labels from the data driven approach on the training set of 50 individuals from the AR face dataset[5]....
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Journal Article•
2,952 citations
"Quality assessment based denoising ..." refers methods in this paper
...As discussed in Section 4, SVM model is learned using the training labels from the data driven approach on the training set of 50 individuals from the AR face dataset[5]....
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...The experiments are conducted on the AR face database [5] containing 756 frontal face images pertaining to 126 subjects (i....
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TL;DR: An adaptive, data-driven threshold for image denoising via wavelet soft-thresholding derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution widely used in image processing applications.
Abstract: The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The proposed threshold is simple and closed-form, and it is adaptive to each subband because it depends on data-driven estimates of the parameters. Experimental results show that the proposed method, called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image assumed known. It also outperforms SureShrink (Donoho and Johnstone 1994, 1995; Donoho 1995) most of the time. The second part of the paper attempts to further validate claims that lossy compression can be used for denoising. The BayesShrink threshold can aid in the parameter selection of a coder designed with the intention of denoising, and thus achieving simultaneous denoising and compression. Specifically, the zero-zone in the quantization step of compression is analogous to the threshold value in the thresholding function. The remaining coder design parameters are chosen based on a criterion derived from Rissanen's minimum description length (MDL) principle. Experiments show that this compression method does indeed remove noise significantly, especially for large noise power. However, it introduces quantization noise and should be used only if bitrate were an additional concern to denoising.
2,917 citations
"Quality assessment based denoising ..." refers background or methods in this paper
...The training labels for the parameter selection are 1For further details of BayesShrink, refer to Chang et al.[1]....
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...In the proposed quality assessment based denoising framework, wavelet based soft thresholding technique is used for denoising, also known as BayesShrink [1]....
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...∙ Each of these corrupt training-probe-set are denoised with the wavelet based BayesShrink denoising algorithm[1] with each of the 𝑖 candidate parameters 𝑃1..𝑖. ∙ The quality vector for each image with quality scores [𝑄1, 𝑄2] is computed and used as the training sample for a multi-class SVM classifier....
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...Several enhancement methods have been proposed in literature to handle these corruptions [1]....
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...∙ Each of these corrupt training-probe-set are denoised with the wavelet based BayesShrink denoising algorithm[1] with each of the i candidate parameters P1....
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