Afzel Noore

Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing

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.

Evolutionary neural network modeling for software cumulative failure time prediction

Abstract An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg–Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches.

Plastic Surgery: A New Dimension to Face Recognition

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.

Feature based RDWT watermarking for multimodal biometric system

This paper presents a 3-level RDWT biometric watermarking algorithm to embed the voice biometric MFC coefficients in a color face image of the same individual for increased robustness, security and accuracy. Phase congruency model is used to compute the embedding locations which preserves the facial features from being watermarked and ensures that the face recognition accuracy is not compromised. The proposed watermarking algorithm uses adaptive user-specific watermarking parameters for improved performance. Using face, voice and multimodal recognition algorithms, and statistical evaluation, we show that the proposed RDWT watermarking algorithm is robust to different frequency and geometric attacks, and provides the multimodal biometric verification accuracy of 94%.

Face recognition with disguise and single gallery images

This paper presents a face recognition algorithm that addresses two major challenges. The first is when an individual intentionally alters the appearance and features using disguises, and the second is when limited gallery images are available for recognition. The algorithm uses a dynamic neural network architecture to extract the phase features of the face texture using 2D log polar Gabor transform. The phase features are divided into frames which are matched using the Hamming distance. The performance of the proposed algorithm is evaluated using three databases that comprise of real and synthetic face images with different disguise artifacts. The performance of the algorithm is evaluated for decreasing number of gallery images and various types of disguises. In all cases the proposed algorithm shows a better performance compared to other existing algorithms.

A Mosaicing Scheme for Pose-Invariant Face Recognition

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 users 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.

A secure conditional access system using digital signature and encryption

A new conditional access system architecture is proposed. It uses XML digital signature and encryption to securely distribute audio, video, image, and data on the Web. It also supports payment transactions in a secure environment.

Hierarchical fusion of multi-spectral face images for improved recognition performance

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.

Multimodal Medical Image Fusion Using Redundant Discrete Wavelet Transform

Medical image fusion has revolutionized medical analysis by improving the precision and performance of computer assisted diagnosis. In this research, a fusion algorithm is proposed to combine pairs of multispectral magnetic resonance imaging such as T1, T2 and Proton Density brain images. The proposed algorithm utilizes different features of Redundant Discrete Wavelet Transform, mutual information based non-linear registration and entropy information to improve performance. Experiments on the BrainWeb database show that the proposed fusion algorithm preserves both edge and component information, and provides improved performance compared to existing Discrete Wavelet Transform based fusion algorithms.

Age transformation for improving face recognition performance

This paper presents a novel age transformation algorithm to handle the challenge of facial aging in face recognition. The proposed algorithm registers the gallery and probe face images in polar coordinate domain and minimizes the variations in facial features caused due to aging. The efficacy of the proposed age transformation algorithm is validated using 2D log polar Gabor based face recognition algorithm on a face database that comprises of face images with large age progression. Experimental results show that the proposed algorithm significantly improves the verification and identification performance.