SVM Based Adaptive Biometric Image Enhancement Using Quality Assessment
01 Jan 2008-pp 351-371
TL;DR: A SVM quality enhancement algorithm is proposed which simultaneously applies selected enhancement algorithms to the original image and selects the best quality regions from the global enhanced image to generate single high quality image.
Abstract: The quality of input data has an important role in the performance of a biometric system Images such as fingerprint and face captured under non-ideal conditions may require additional preprocessing This chapter presents intelligent SVM techniques for quality assessment and enhancement The proposed quality assessment algorithm associates the quantitative quality score of the image that has a specific type of irregularity such as noise, blur, and illumination This enables the application of the most appropriate quality enhancement algorithm on the non-ideal image We further propose a SVM quality enhancement algorithm which simultaneously applies selected enhancement algorithms to the original image and selects the best quality regions from the global enhanced image These selected regions are used to generate single high quality image The performance of the proposed algorithms is validated by considering face biometrics as the case study Results show that the proposed algorithms improve the verification accuracy of face recognition by around 10–17%
Citations
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01 Aug 2008
TL;DR: This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition.
Abstract: This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.
285 citations
TL;DR: A new face image quality index (FQI) is proposed that combines multiple quality measures, and classifies a face image based on this index, and conducts statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.
Abstract: The performance of an automated face recognition system can be significantly influenced by face image quality. Designing effective image quality index is necessary in order to provide real-time feedback for reducing the number of poor quality face images acquired during enrollment and authentication, thereby improving matching performance. In this study, the authors first evaluate techniques that can measure image quality factors such as contrast, brightness, sharpness, focus and illumination in the context of face recognition. Second, they determine whether using a combination of techniques for measuring each quality factor is more beneficial, in terms of face recognition performance, than using a single independent technique. Third, they propose a new face image quality index (FQI) that combines multiple quality measures, and classifies a face image based on this index. In the author's studies, they evaluate the benefit of using FQI as an alternative index to independent measures. Finally, they conduct statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.
74 citations
TL;DR: The proposed wavelet-based image denoising using LS-SVM can preserve edges very well while removing noise, and can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoised techniques.
Abstract: Image denoising is an important image processing task, both as itself, and as a preprocessing in image processing pipeline. The least squares support vector machine (LS-SVM) has shown to exhibit excellent classification performance in many applications. Based on undecimated discrete wavelet transform, a new wavelet-based image denoising using LS-SVM is proposed in this paper. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the undecimated discrete wavelet transform. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in wavelet domain, and the LS-SVM model is obtained by training. Then the wavelet coefficients are divided into two classes (noisy coefficients and noise-free ones) by LS-SVM training model. Finally, all noisy wavelet coefficients are relatively well denoised by shrink method, in which the adaptive threshold is utilized. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.
68 citations
Proceedings Article•
01 Nov 2012TL;DR: This paper evaluates a number of techniques that measure image quality factors namely, contrast, brightness, focus, sharpness, and illumination and proposes a novel face image quality index (FQI) that combines the five aforementioned quality factors.
Abstract: In biometric studies, quality evaluation of input data is very important, and has proven to have a direct relation with system performance. Quality measures can provide real-time feedback to reduce the number of poor quality submissions to the system. Another benefit is that they can predict and improve the authentication performance (e.g., by using quality-dependent thresholds). This paper main focus is image quality assessment for face recognition. First, we evaluate a number of techniques that measure image quality factors namely, contrast, brightness, focus, sharpness, and illumination. Second, via a set of experiments measuring the sensitivity of each matric to quality change, we select the most practical measure(s) for each quality factor. Finally, we propose a novel face image quality index (FQI) that combines the five aforementioned quality factors. Via a set of statistical significance tests, we illustrate and support that FQI is a promising quality measure that can be used as an alternative to some benchmark face image quality measures.
60 citations
Cites methods from "SVM Based Adaptive Biometric Image ..."
...We also perform a set of experiments to compare the proposed FQI to some benchmark measurements: (UQI) [14], (AVI) [8], and (WB) [13]....
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...There are several image quality measures proposed in the literature, such as (a) Universal Quality Index (UQI) [14], (b) Average Image (AVI) [8], and (c) Wavelet-based (WB) face quality measure [13]....
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TL;DR: The proposed image denoising using support vector machine (SVM) classification in nonsubsampled contourlet transform (NSCT) domain can preserve edges very well while removing noise.
56 citations
References
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Book•
01 Jan 1995TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
40,147 citations
"SVM Based Adaptive Biometric Image ..." refers methods in this paper
...In this chapter, we propose a biometric image quality assessment and enhancement algorithm using Support Vector Machine (SVM) [5] learning to address this challenge....
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...To alleviate these inherent limitations, we use Support Vector machine (SVM) [5] based learning algorithms for biometric image quality assessment and enhancement....
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Book•
01 May 1992TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.
16,073 citations
"SVM Based Adaptive Biometric Image ..." refers methods in this paper
...Usually, Discrete Wavelet Transform (DWT) [ 36 , 37] is used for image based operations such as image fusion, denoising and quality measure because DWT preserves different frequency information and allows good localization both in time and spatial domain....
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TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Abstract: We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
14,562 citations
"SVM Based Adaptive Biometric Image ..." refers methods in this paper
...• Principal component analysis (PCA) [46] is an appearance based face recognition algorithm which computes the principal eigen-vectors from the training face images....
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...The enhancement is performed with all three kernels and verification accuracy for the enhanced images is computed using PCA based face recognition algorithm....
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...Figures 5– 8, show the Receiver Operating Characteristics (ROC) plots for comparison using PCA, FLDA, LFA and texture feature based face verification algorithms....
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...The performance of the proposed quality enhancement algorithm is evalauted using four existing face recognition algorithms, Principal component analysis [46], Fisher linear discriminant analysis [47], Local feature analysis [48] and texture features [49]....
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...Table 2 shows that the maximum improvement in accuracy is 17.16%, which is obtained for PCA based face recognition algorithm....
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TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.
14,157 citations
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Abstract: We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.
11,674 citations
"SVM Based Adaptive Biometric Image ..." refers background or methods in this paper
...• Fisher Linear Discriminant Analysis (FLDA) [ 47 ] is an appearance based face recognition algorithm....
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...The performance of the proposed quality enhancement algorithm is evalauted using four existing face recognition algorithms, Principal component analysis [46], Fisher linear discriminant analysis [ 47 ], Local feature analysis [48] and texture features [49]....
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...Fig. 6. ROC plots comparing the performance of the proposed quality assessment and enhancement algorithm with existing image quality enhancement algorithms using FLDA based face verification algorithm [ 47 ]...
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