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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10 Dec 2002
TL;DR: A no-reference blur metric based on the analysis of the spread of the edges in an image is presented, which is shown to perform well over a range of image content.
Abstract: We present a no-reference blur metric for images and video. The blur metric is based on the analysis of the spread of the edges in an image. Its perceptual significance is validated through subjective experiments. The novel metric is near real-time, has low computational complexity and is shown to perform well over a range of image content. Potential applications include optimization of source coding, network resource management and autofocus of an image capturing device.
643 citations
"Quality assessment based denoising ..." refers background or methods in this paper
...This research focuses on computationally simple quality metrics that possess intuitive relevance and high correlation with face recognition accuracy are considered in this research, namely, No-reference quality assessment (Q1) [8] and Edge spread measure (Q2) [6]....
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...However, the experiments in [6] show that either of the two directions suffices for quality assessment....
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...No-reference [8] Edge spread [6] Gaussian noise 0....
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...[6] proposed edge spread as a measure to estimate irregularities based on edges and their adjacent regions....
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...∙ Each artifact kernel is applied on a new copy of the probe set and the average of no-reference quality score [8] and blur assessment score [6] are computed individually....
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TL;DR: Both quality indices for fingerprint images are developed and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94% in EER is observed on the FVC2002 DB3 database.
Abstract: The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94% in EER is observed on the FVC2002 DB3 database.
292 citations
"Quality assessment based denoising ..." refers background in this paper
...In literature, several research papers exist on analyzing the effects of quality on the performance of different biometric modalities such as iris and fingerprint [2, 3, 4]....
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01 May 2010
TL;DR: This paper designs a fully automated iris image quality evaluation block that estimates defocus blur, motion blur, off-angle, occlusion, lighting, specular reflection, and pixel counts and fuses the estimated factors by using a Dempster-Shafer theory approach to evidential reasoning.
Abstract: Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is one of the most reliable biometrics in terms of recognition and identification performance. However, the performance of these systems is affected by poor-quality imaging. In this paper, we extend iris quality assessment research by analyzing the effect of various quality factors such as defocus blur, off-angle, occlusion/specular reflection, lighting, and iris resolution on the performance of a traditional iris recognition system. We further design a fully automated iris image quality evaluation block that estimates defocus blur, motion blur, off-angle, occlusion, lighting, specular reflection, and pixel counts. First, each factor is estimated individually, and then, the second step fuses the estimated factors by using a Dempster-Shafer theory approach to evidential reasoning. The designed block is evaluated on three data sets: Institute of Automation, Chinese Academy of Sciences (CASIA) 3.0 interval subset, West Virginia University (WVU) non-ideal iris, and Iris Challenge Evaluation (ICE) 1.0 dataset made available by National Institute for Standards and Technology (NIST). Considerable improvement in recognition performance is demonstrated when removing poor-quality images selected by our quality metric. The upper bound on computational complexity required to evaluate the quality of a single image is O(n2 log n).
128 citations
"Quality assessment based denoising ..." refers background in this paper
...In literature, several research papers exist on analyzing the effects of quality on the performance of different biometric modalities such as iris and fingerprint [2, 3, 4]....
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17 Jun 2006
TL;DR: A method using local features to assess the quality of an image, with demonstration in biometrics, is proposed, using the orientation tensor with a set of symmetry descriptors, which can be varied according to the application.
Abstract: A method using local features to assess the quality of an image, with demonstration in biometrics, is proposed. Recently, image quality awareness has been found to increase recognition rates and to support decisions in multimodal authentication systems significantly. Nevertheless, automatic quality assessment is still an open issue, especially with regard to general tasks. Indicators of perceptual quality like noise, lack of structure, blur, etc. can be retrieved from the orientation tensor of an image, but there are few studies reporting on this. Here we study the orientation tensor with a set of symmetry descriptors, which can be varied according to the application. Allowed classes of local shapes are generically provided by the user but no training or explicit reference information is required. Experimental results are given for fingerprint. Furthermore, we indicate the applicability of the proposed method to face images.
67 citations
"Quality assessment based denoising ..." refers background in this paper
...In literature, several research papers exist on analyzing the effects of quality on the performance of different biometric modalities such as iris and fingerprint [2, 3, 4]....
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