The quality of input data has an important role in the performance of a biometric system. Images ... more 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
[1] AK Jain, L. Hong, and R. Bolle, On-line fingerprint verification, IEEE Trans. PAMI, vol. 19... more [1] AK Jain, L. Hong, and R. Bolle, On-line fingerprint verification, IEEE Trans. PAMI, vol. 19, no. 4, pp. 302314, 1997. [2] Y.-H. Pang, A. Teoh BJ, and David Ngo CL, Enhanced pseudo Zernike moments in face recognition, IEICE Electron. Express, vol. 2, no. 3, pp. 7075, ...
In this paper three algorithms for iris verification have been presented. Iris detection algorith... more In this paper three algorithms for iris verification have been presented. Iris detection algorithms include the normalization and iris extraction steps. Three algorithms for verification process are (a) Algorithm using radial and circular features, (b) Algorithm using Fourier transforms and (c) Algorithm using Circular-Mellin transforms. Proposed algorithms have been tested on CASIA database and some non-infrared Iris images. The experimental results show that the algorithm based on Circular – Mellin Transform gives the best result with an accuracy of 95.45%. Some initial experiments on non-infrared iris images shows that this algorithm can work on such images but it still requires some more attention and this is our future work.
This paper presents a Dempster Shafer theory based classifier fusion algorithm to improve the per... more This paper presents a Dempster Shafer theory based classifier fusion algorithm to improve the performance of fingerprint verification. The proposed fusion algorithm combines decision induced match scores of minutiae, ridge, fingercode and pore based fingerprint verification algorithms and provides an improvement of at least 8.1% in the verification accuracy compared to the individual algorithms. Further, proposed fusion algorithm outperforms by at least 2.52% when compared with existing fusion algorithms. We also found that the use of Dempster’s rule of conditioning reduces the training time by approximately 191 seconds.
International Journal of Approximate Reasoning, 2009
Existing algorithms that fuse level-2 and level-3 fingerprint match scores perform well when the ... more Existing algorithms that fuse level-2 and level-3 fingerprint match scores perform well when the number of features are adequate and the quality of images are acceptable. In practice, fingerprints collected under unconstrained environment neither guarantee the requisite image quality nor the minimum number of features required. This paper presents a novel fusion algorithm that combines fingerprint match scores to provide high accuracy under non-ideal conditions. The match scores obtained from level-2 and level-3 classifiers are first augmented with a quality score that is quantitatively determined by applying redundant discrete wavelet transform to a fingerprint image. We next apply the generalized belief functions of Dezert-Smarandache theory to effectively fuse the quality-augmented match scores obtained from level-2 and level-3 classifiers. Unlike statistical and learning based fusion techniques, the proposed plausible and paradoxical reasoning approach effectively mitigates conflicting decisions obtained from classifiers especially when the evidences are imprecise due to poor image quality or limited fingerprint features. The proposed quality-augmented fusion algorithm is validated using a comprehensive database which comprises of rolled and partial fingerprint images of varying quality with arbitrary number of features. The performance is compared with existing fusion approaches for different challenging realistic scenarios.
This paper presents a two level hierarchical fusion of face images captured under visible and inf... more This paper presents a two level hierarchical fusion of face images captured under visible and infrared light spectrum to improve the performance of face recognition. At image level fusion, two face images from different spectrums are fused using DWT based fusion algorithm. At feature level fusion, the amplitude and phase features are extracted from the fused image using 2D log polar Gabor wavelet. An adaptive SVM learning algorithm intelligently selects either the amplitude or phase features to generate a fused feature set for improved face recognition. The recognition performance is observed under the worst case scenario of using single training images. Experimental results on Equinox face database show that the combination of visible light and short-wave IR spectrum face images yielded the best recognition performance with an equal error rate of 2.86%. The proposed image-feature fusion algorithm also performed better than existing fusion algorithms.
IEEE Transactions on Systems, Man, and Cybernetics, 2008
This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, an... more 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.
In this paper, we propose a 2-stage preprocessing framework which consists of image enhancement a... more In this paper, we propose a 2-stage preprocessing framework which consists of image enhancement and deformation techniques to increase the verification performance of image-based biometric systems. In the preprocessing framework, first the quality of biometric image is enhanced and then a deformation model is applied to minimize the variation between the two images to be matched. The proposed SVM image quality enhancement algorithm selects good quality regions from different globally enhanced images and combines them to generate a single high-quality feature-rich image. The proposed deformation algorithm is based on phase congruency information and locally minimizes the variations between two images while retaining the features required for recognition. The proposed algorithms are validated using face and iris biometrics as the two case studies. For performance evaluation, different face and iris recognition algorithms are chosen and the verification accuracy is computed using non-homogenous face and iris databases. Experimental results show that the performance of face and iris recognition algorithms are significantly improved when the input images are preprocessed using the proposed enhancement and deformation algorithms. r
This paper presents a novel digital watermarking technique using face and demographic text data a... more This paper presents a novel digital watermarking technique using face and demographic text data as multiple watermarks for verifying the chain of custody and protecting the integrity of a fingerprint image. The watermarks are embedded in selected texture regions of a fingerprint image using Discrete Wavelet Transform. Experimental results show that modifications in these locations are visually imperceptible and maintain the minutiae details. The integrity of the fingerprint image is verified through the high matching scores obtained from an Automatic Fingerprint Identification System. There is also a high degree of visual correlation between the embedded images, and the extracted images from the watermarked fingerprint. The degree of similarity is computed using pixel-based metrics and human visual system metrics. The results also show that the proposed watermarked fingerprint and the extracted images are resilient to common attacks such as compression, filtering, and noise.
This paper presents a novel multi-level wavelet based fusion algorithm that combines information ... more This paper presents a novel multi-level wavelet based fusion algorithm that combines information from fingerprint, face, iris, and signature images of an individual into a single composite image. The proposed approach reduces the memory size, increases the recognition accuracy using multimodal biometric features, and withstands common attacks such as smoothing, cropping, JPEG 2000, and filtering due to tampering. The fusion algorithm is validated using the verification algorithms we developed, existing algorithms, and commercial algorithm. In addition to our multimodal database, experiments are also performed on other well known databases such as FERET face database and CASIA iris database. The effectiveness of the fusion algorithm is experimentally validated by computing the matching scores and the equal error rates before fusion, after reconstruction of biometric images, and when the composite fused image is subjected to both frequency and geometric attacks. The results show that the fusion process reduced the memory required for storing the multimodal images by 75%. The integrity of biometric features and the recognition performance of the resulting composite fused image is not affected. The complexity of the fusion and the reconstruction algorithms is O(n log n) and is suitable for many real-time applications. We also propose a multimodal biometric algorithm that further reduces the equal error rate compared to individual biometric images.
The performance of score-level fusion algorithms is often affected by conflicting decisions gener... more The performance of score-level fusion algorithms is often affected by conflicting decisions generated by the constituent matchers/classifiers. This paper describes a fusion algorithm that incorporates the likelihood ratio test statistic in a support vector machine (SVM) framework in order to classify match scores originating from multiple matchers. The proposed approach also takes into account the precision and uncertainties of individual matchers. The resulting fusion algorithm is used to mitigate the effect of covariate factors in face recognition by combining the match scores of linear appearance-based face recognition algorithms with their non-linear counterparts. Experimental results on a heterogeneous face database of 910 subjects suggest that the proposed fusion algorithm can significantly improve the verification performance of a face recognition system. Thus, the contribution of this work is two-fold: (a) the design of a novel fusion technique that incorporates the likelihood ratio test-statistic in a SVM fusion framework; and (b) the application of the technique to face recognition in order to mitigate the effect of covariate factors.
We describe a face mosaicing scheme that generates a composite face image during enrollment based... more We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semi-profile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image. In the proposed scheme, the side profile images are aligned with the frontal image using a terrain transform that exploits neighborhood properties to determine the transformation relating the 2 images. Multiresolution splining is then used to blend the side profiles with the frontal image thereby generating a composite face image of the user. A local binary pattern matching algorithm is used to compare an input face image with the template face mosaic. Experiments conducted on a small dataset of 27 users indicate that face mosaicing as described in this paper offers significant benefits by accounting for the pose variations commonly observed in face images.
This paper presents a combined DWT and LSB based biometric watermarking algorithm that securely e... more This paper presents a combined DWT and LSB based biometric watermarking algorithm that securely embeds a face template in a fingerprint image. The proposed algorithm is robust to geometric and frequency attacks and protects the integrity of both the face template and the fingerprint image. Experimental results performed on a database of 750 face and 750 fingerprint images show that the algorithm has the advantages of both the existing DWT and LSB based algorithms. A multimodal biometric algorithm is used as a metric to evaluate the combined performance of both face and fingerprint recognition.
In this paper, a detailed experimental study of face detection algorithms based on "Skin Color" h... more In this paper, a detailed experimental study of face detection algorithms based on "Skin Color" has been made. Three color spaces, RGB, YCbCr and HSI are of main concern. We have compared the algorithms based on these color spaces and have combined them to get a new skin color based face detection algorithm which gives higher accuracy. Experimental results show that the proposed algorithm is good enough to localize a human face in an image with an accuracy of 95.18%.
IEEE Transactions on Systems, Man, and Cybernetics, 2009
This paper formulates an evidence-theoretic multimodal unification approach using belief function... more This paper formulates an evidence-theoretic multimodal unification approach using belief functions that takes into account the variability in biometric image characteristics. While processing non-ideal images the variation in the quality of features at different levels of abstraction may cause individual classifiers to generate conflicting genuine-impostor decisions. Existing fusion approaches are non-adaptive and do not always guarantee optimum performance improvements. We propose a contextual unification framework to dynamically select the most appropriate evidence-theoretic fusion algorithm for a given scenario. In the first approach, the unification framework uses deterministic rules to select the most appropriate fusion algorithm; while in the second approach, the framework intelligently learns from the input evidences using a 2ν-granular support vector machine. The effectiveness of our unification approach is experimentally validated by fusing match scores from level-2 and level-3 fingerprint features. Compared to existing fusion algorithms, the proposed unification approach is computationally efficient, and the verification accuracy is not compromised even when conflicting decisions are encountered.
IEEE Transactions on Information Forensics and Security, 2010
Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial ... more Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a large extent thereby posing a great challenge for face recognition algorithms. The contribution of this research is 1) preparing a face database of 900 individuals for plastic surgery, and 2) providing an analytical and experimental underpinning of the effect of plastic surgery on face recognition algorithms. The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance. Therefore, it is imperative to initiate a research effort so that future face recognition systems will be able to address this important problem.
IEEE Transactions on Systems, Man, and Cybernetics, 2007
Mosaicing entails the consolidation of information represented by multiple images through the app... more Mosaicing entails the consolidation of information represented by multiple images through the application of a registration and blending procedure. We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semiproflle face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image. In the proposed scheme, the side profile images are aligned with the frontal image using a hierarchical registration algorithm that exploits neighborhood properties to determine the transformation relating the two images. Multiresolution splining is then used to blend the side profiles with the frontal image, thereby generating a composite face image of the user. A texture-based face recognition technique that is a slightly modified version of the C2 algorithm proposed by Serre et al. is used to compare a probe face image with the gallery face mosaic. Experiments conducted on three different databases indicate that face mosaicing, as described in this paper, offers significant benefits by accounting for the pose variations that are commonly observed in face images.
... which consists of Mayank Vatsa, Richa Singh and Afzel Noore are with the Lane Department of C... more ... which consists of Mayank Vatsa, Richa Singh and Afzel Noore are with the Lane Department of CSEE, West Virginia University, Morgantown, 26505, USA (e-mail: mayankv, richas, [email protected]).* the ordered sequence ...
The quality of input data has an important role in the performance of a biometric system. Images ... more 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
[1] AK Jain, L. Hong, and R. Bolle, On-line fingerprint verification, IEEE Trans. PAMI, vol. 19... more [1] AK Jain, L. Hong, and R. Bolle, On-line fingerprint verification, IEEE Trans. PAMI, vol. 19, no. 4, pp. 302314, 1997. [2] Y.-H. Pang, A. Teoh BJ, and David Ngo CL, Enhanced pseudo Zernike moments in face recognition, IEICE Electron. Express, vol. 2, no. 3, pp. 7075, ...
In this paper three algorithms for iris verification have been presented. Iris detection algorith... more In this paper three algorithms for iris verification have been presented. Iris detection algorithms include the normalization and iris extraction steps. Three algorithms for verification process are (a) Algorithm using radial and circular features, (b) Algorithm using Fourier transforms and (c) Algorithm using Circular-Mellin transforms. Proposed algorithms have been tested on CASIA database and some non-infrared Iris images. The experimental results show that the algorithm based on Circular – Mellin Transform gives the best result with an accuracy of 95.45%. Some initial experiments on non-infrared iris images shows that this algorithm can work on such images but it still requires some more attention and this is our future work.
This paper presents a Dempster Shafer theory based classifier fusion algorithm to improve the per... more This paper presents a Dempster Shafer theory based classifier fusion algorithm to improve the performance of fingerprint verification. The proposed fusion algorithm combines decision induced match scores of minutiae, ridge, fingercode and pore based fingerprint verification algorithms and provides an improvement of at least 8.1% in the verification accuracy compared to the individual algorithms. Further, proposed fusion algorithm outperforms by at least 2.52% when compared with existing fusion algorithms. We also found that the use of Dempster’s rule of conditioning reduces the training time by approximately 191 seconds.
International Journal of Approximate Reasoning, 2009
Existing algorithms that fuse level-2 and level-3 fingerprint match scores perform well when the ... more Existing algorithms that fuse level-2 and level-3 fingerprint match scores perform well when the number of features are adequate and the quality of images are acceptable. In practice, fingerprints collected under unconstrained environment neither guarantee the requisite image quality nor the minimum number of features required. This paper presents a novel fusion algorithm that combines fingerprint match scores to provide high accuracy under non-ideal conditions. The match scores obtained from level-2 and level-3 classifiers are first augmented with a quality score that is quantitatively determined by applying redundant discrete wavelet transform to a fingerprint image. We next apply the generalized belief functions of Dezert-Smarandache theory to effectively fuse the quality-augmented match scores obtained from level-2 and level-3 classifiers. Unlike statistical and learning based fusion techniques, the proposed plausible and paradoxical reasoning approach effectively mitigates conflicting decisions obtained from classifiers especially when the evidences are imprecise due to poor image quality or limited fingerprint features. The proposed quality-augmented fusion algorithm is validated using a comprehensive database which comprises of rolled and partial fingerprint images of varying quality with arbitrary number of features. The performance is compared with existing fusion approaches for different challenging realistic scenarios.
This paper presents a two level hierarchical fusion of face images captured under visible and inf... more This paper presents a two level hierarchical fusion of face images captured under visible and infrared light spectrum to improve the performance of face recognition. At image level fusion, two face images from different spectrums are fused using DWT based fusion algorithm. At feature level fusion, the amplitude and phase features are extracted from the fused image using 2D log polar Gabor wavelet. An adaptive SVM learning algorithm intelligently selects either the amplitude or phase features to generate a fused feature set for improved face recognition. The recognition performance is observed under the worst case scenario of using single training images. Experimental results on Equinox face database show that the combination of visible light and short-wave IR spectrum face images yielded the best recognition performance with an equal error rate of 2.86%. The proposed image-feature fusion algorithm also performed better than existing fusion algorithms.
IEEE Transactions on Systems, Man, and Cybernetics, 2008
This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, an... more 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.
In this paper, we propose a 2-stage preprocessing framework which consists of image enhancement a... more In this paper, we propose a 2-stage preprocessing framework which consists of image enhancement and deformation techniques to increase the verification performance of image-based biometric systems. In the preprocessing framework, first the quality of biometric image is enhanced and then a deformation model is applied to minimize the variation between the two images to be matched. The proposed SVM image quality enhancement algorithm selects good quality regions from different globally enhanced images and combines them to generate a single high-quality feature-rich image. The proposed deformation algorithm is based on phase congruency information and locally minimizes the variations between two images while retaining the features required for recognition. The proposed algorithms are validated using face and iris biometrics as the two case studies. For performance evaluation, different face and iris recognition algorithms are chosen and the verification accuracy is computed using non-homogenous face and iris databases. Experimental results show that the performance of face and iris recognition algorithms are significantly improved when the input images are preprocessed using the proposed enhancement and deformation algorithms. r
This paper presents a novel digital watermarking technique using face and demographic text data a... more This paper presents a novel digital watermarking technique using face and demographic text data as multiple watermarks for verifying the chain of custody and protecting the integrity of a fingerprint image. The watermarks are embedded in selected texture regions of a fingerprint image using Discrete Wavelet Transform. Experimental results show that modifications in these locations are visually imperceptible and maintain the minutiae details. The integrity of the fingerprint image is verified through the high matching scores obtained from an Automatic Fingerprint Identification System. There is also a high degree of visual correlation between the embedded images, and the extracted images from the watermarked fingerprint. The degree of similarity is computed using pixel-based metrics and human visual system metrics. The results also show that the proposed watermarked fingerprint and the extracted images are resilient to common attacks such as compression, filtering, and noise.
This paper presents a novel multi-level wavelet based fusion algorithm that combines information ... more This paper presents a novel multi-level wavelet based fusion algorithm that combines information from fingerprint, face, iris, and signature images of an individual into a single composite image. The proposed approach reduces the memory size, increases the recognition accuracy using multimodal biometric features, and withstands common attacks such as smoothing, cropping, JPEG 2000, and filtering due to tampering. The fusion algorithm is validated using the verification algorithms we developed, existing algorithms, and commercial algorithm. In addition to our multimodal database, experiments are also performed on other well known databases such as FERET face database and CASIA iris database. The effectiveness of the fusion algorithm is experimentally validated by computing the matching scores and the equal error rates before fusion, after reconstruction of biometric images, and when the composite fused image is subjected to both frequency and geometric attacks. The results show that the fusion process reduced the memory required for storing the multimodal images by 75%. The integrity of biometric features and the recognition performance of the resulting composite fused image is not affected. The complexity of the fusion and the reconstruction algorithms is O(n log n) and is suitable for many real-time applications. We also propose a multimodal biometric algorithm that further reduces the equal error rate compared to individual biometric images.
The performance of score-level fusion algorithms is often affected by conflicting decisions gener... more The performance of score-level fusion algorithms is often affected by conflicting decisions generated by the constituent matchers/classifiers. This paper describes a fusion algorithm that incorporates the likelihood ratio test statistic in a support vector machine (SVM) framework in order to classify match scores originating from multiple matchers. The proposed approach also takes into account the precision and uncertainties of individual matchers. The resulting fusion algorithm is used to mitigate the effect of covariate factors in face recognition by combining the match scores of linear appearance-based face recognition algorithms with their non-linear counterparts. Experimental results on a heterogeneous face database of 910 subjects suggest that the proposed fusion algorithm can significantly improve the verification performance of a face recognition system. Thus, the contribution of this work is two-fold: (a) the design of a novel fusion technique that incorporates the likelihood ratio test-statistic in a SVM fusion framework; and (b) the application of the technique to face recognition in order to mitigate the effect of covariate factors.
We describe a face mosaicing scheme that generates a composite face image during enrollment based... more We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semi-profile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image. In the proposed scheme, the side profile images are aligned with the frontal image using a terrain transform that exploits neighborhood properties to determine the transformation relating the 2 images. Multiresolution splining is then used to blend the side profiles with the frontal image thereby generating a composite face image of the user. A local binary pattern matching algorithm is used to compare an input face image with the template face mosaic. Experiments conducted on a small dataset of 27 users indicate that face mosaicing as described in this paper offers significant benefits by accounting for the pose variations commonly observed in face images.
This paper presents a combined DWT and LSB based biometric watermarking algorithm that securely e... more This paper presents a combined DWT and LSB based biometric watermarking algorithm that securely embeds a face template in a fingerprint image. The proposed algorithm is robust to geometric and frequency attacks and protects the integrity of both the face template and the fingerprint image. Experimental results performed on a database of 750 face and 750 fingerprint images show that the algorithm has the advantages of both the existing DWT and LSB based algorithms. A multimodal biometric algorithm is used as a metric to evaluate the combined performance of both face and fingerprint recognition.
In this paper, a detailed experimental study of face detection algorithms based on "Skin Color" h... more In this paper, a detailed experimental study of face detection algorithms based on "Skin Color" has been made. Three color spaces, RGB, YCbCr and HSI are of main concern. We have compared the algorithms based on these color spaces and have combined them to get a new skin color based face detection algorithm which gives higher accuracy. Experimental results show that the proposed algorithm is good enough to localize a human face in an image with an accuracy of 95.18%.
IEEE Transactions on Systems, Man, and Cybernetics, 2009
This paper formulates an evidence-theoretic multimodal unification approach using belief function... more This paper formulates an evidence-theoretic multimodal unification approach using belief functions that takes into account the variability in biometric image characteristics. While processing non-ideal images the variation in the quality of features at different levels of abstraction may cause individual classifiers to generate conflicting genuine-impostor decisions. Existing fusion approaches are non-adaptive and do not always guarantee optimum performance improvements. We propose a contextual unification framework to dynamically select the most appropriate evidence-theoretic fusion algorithm for a given scenario. In the first approach, the unification framework uses deterministic rules to select the most appropriate fusion algorithm; while in the second approach, the framework intelligently learns from the input evidences using a 2ν-granular support vector machine. The effectiveness of our unification approach is experimentally validated by fusing match scores from level-2 and level-3 fingerprint features. Compared to existing fusion algorithms, the proposed unification approach is computationally efficient, and the verification accuracy is not compromised even when conflicting decisions are encountered.
IEEE Transactions on Information Forensics and Security, 2010
Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial ... more Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a large extent thereby posing a great challenge for face recognition algorithms. The contribution of this research is 1) preparing a face database of 900 individuals for plastic surgery, and 2) providing an analytical and experimental underpinning of the effect of plastic surgery on face recognition algorithms. The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance. Therefore, it is imperative to initiate a research effort so that future face recognition systems will be able to address this important problem.
IEEE Transactions on Systems, Man, and Cybernetics, 2007
Mosaicing entails the consolidation of information represented by multiple images through the app... more Mosaicing entails the consolidation of information represented by multiple images through the application of a registration and blending procedure. We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semiproflle face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image. In the proposed scheme, the side profile images are aligned with the frontal image using a hierarchical registration algorithm that exploits neighborhood properties to determine the transformation relating the two images. Multiresolution splining is then used to blend the side profiles with the frontal image, thereby generating a composite face image of the user. A texture-based face recognition technique that is a slightly modified version of the C2 algorithm proposed by Serre et al. is used to compare a probe face image with the gallery face mosaic. Experiments conducted on three different databases indicate that face mosaicing, as described in this paper, offers significant benefits by accounting for the pose variations that are commonly observed in face images.
... which consists of Mayank Vatsa, Richa Singh and Afzel Noore are with the Lane Department of C... more ... which consists of Mayank Vatsa, Richa Singh and Afzel Noore are with the Lane Department of CSEE, West Virginia University, Morgantown, 26505, USA (e-mail: mayankv, richas, [email protected]).* the ordered sequence ...
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