Department of Computer Science & Engineering

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Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

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IIT Jodhpur

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. © 2008 IEEE.

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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing

Most of the attacks against (reduced) SHA-2 family in literature have used local collisions which are valid for linearized version of SHA-2 hash functions. Recently, at FSE '08, an attack against reduced round SHA-256 was presented by Nikolić and Biryukov which used a local collision which is valid for the actual SHA-256 function. It is a 9-step local collision which starts by introducing a modular difference of 1 in the two messages. It succeeds with probability roughly 1/3. We build on the work of Nikolić and Biryukov and provide a generalized nonlinear local collision which accepts an arbitrary initial message difference. This local collision succeeds with probability 1. Using this local collision we present attacks against 18-step SHA-256 and 18-step SHA-512 with arbitrary initial difference. Both of these attacks succeed with probability 1. We then present special cases of our local collision and show two different differential paths for attacking 20-step SHA-256 and 20-step SHA-512. One of these paths is the same as presented by Nikolić and Biryukov while the other one is a new differential path. Messages following both these differential paths can be found with probability 1. This improves on the previous result where the success probability of 20-step attack was 1/3. Finally, we present two differential paths for 21-step collisions for SHA-256 and SHA-512, one of which is a new path. The success probabilities of these paths for SHA-256 are roughly 2-∈15 and 2-∈17 which improve on the 21-step attack having probability 2-∈19 reported earlier. We show examples of message pairs following all the presented differential paths for up to 21-step collisions in SHA-256. We also show first real examples of colliding message pairs for up to 20-step reduced SHA-512. © 2008 Springer-Verlag Berlin Heidelberg.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Non-linear reduced round attacks against SHA-2 Hash family

We propose a novel framework for object detection and localization in images containing appreciable clutter and occlusions. The problem is cast in a statistical hypothesis testing framework. The image under test is converted into a set of local features using affine invariant local region detectors, described using the popular SIFT descriptor. Due to clutter and occlusions, this set is expected to contain features which do not belong to the object. We sample subsets of local features from this set and test for the alternate hypothesis of object present against the null hypothesis of object absent. Further, we use a method similar to the recently proposed spatial scan statistic to refine the object localization estimates obtained from the sampling process. We demonstrate the results of our method on the two datasets TUD Motorbikes and TUD Cars. TUD Cars database has background clutter. TUD Motorbikes dataset is recognized to have substantial variation in terms of scale, background, illumination, viewpoint and occlusions. © 2008 IEEE.

Proceedings - 6th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2008

Object category detection by statistical test of hypothesis

In this paper, we propose a novel framework for automated analysis of surveillance videos. By analysis, we imply summarizing and mining of the information in the video for learning usual patterns and discovering unusual ones. We approach this video analysis problem by acknowledging that a video contains information at multiple levels and in multiple attributes. Each such component and cooccurrences of these component values play an important role in characterizing an event as usual or unusual. Therefore, we cluster the video data at multiple levels of abstraction and in multiple attributes and view these clusters as a summary of the information in the video. We apply cluster algebra to mine this summary from multiple perspectives and to adapt association learning for automated selection of components because of which the event is unusual. We also propose a novel incremental clustering algorithm. © 2008 IEEE.

A framework for analysis of surveillance videos

In this paper a video coding scheme based on parametric compression of texture is proposed. Each macro block is characterized either as an edge block, or as a non edge block containing texture. The non edge blocks are coded by modeling them as an autoregressive process (AR). By applying the AR model in spatio-temporal domain, we ensure both spatial as well as temporal consistency. Edge blocks are encoded using the standard H.264/AVC. The proposed algorithm achieves upto 54.52% more compression as compared to the standard H.264/AVC at similar visual quality. © 2008 IEEE.

Parametric video compression scheme using ar based texture synthesis

This paper presents an integrated image fusion and match score fusion of multispectral face images. The fusion of visible and long wave infrared face images is performed using 2 ν-granular SVM which uses multiple SVMs to learn both the local and global properties of the multispectral face images at different granularity levels and resolution. The 2 ν-GSVM performs accurate classification which is subsequently used to dynamically compute the weights of visible and infrared images for generating a fused face image. 2D log polar Gabor transform and local binary pattern feature extraction algorithms are applied to the fused face image to extract global and local facial features, respectively. The corresponding match scores are fused using Dezert Smarandache theory of fusion which is based on plausible and paradoxical reasoning. The efficacy of the proposed algorithm is validated using the Notre Dame and Equinox databases and is compared with existing statistical, learning, and evidence theory based fusion algorithms. © 2007 Pattern Recognition Society.

Fulltext

Pattern Recognition

Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition

Video authentication is often presented as evidence in many criminal cases. Therefore, the authenticity of the video data is of paramount interest. This paper presents an intelligent video authentication algorithm using support vector machine. The proposed algorithm does not require the computation and storage of secret key or embedding of watermark. It computes the local relative correlation information and classifies the video as tampered or non-tampered. Performance of the proposed algorithm is not affected by acceptable video processing operations such as compression and scaling and effectively classifies the tampered videos. On a database of 795 videos, the proposed algorithm outperforms the existing algorithm by 18.5%. © 2008 Springer-Verlag Berlin Heidelberg.

Studies in Computational Intelligence

Video authentication using relative correlation information and SVM

We propose a novel approach to 3D hand geometry-based person authentication using projected light patterns. Instead of explicitly c omputing a depth map of the palm for recognition, we capture the depth information in the deformations of a projected texture pattern, and use it directly for recognition. The deformed pattern is c haracterized using local texture measures, which can encode the certain depth characteristics of the palm. An authentication system built using the proposed technique achieves an equal error rate of 0.84% on a dataset of 1341 samples collected from 149 users, as opposed to 4.03% using traditional 2D features on an identical dataset. The approach is robust as well as computationally efficient and could be applied to other 3D object recognition problems as well. © 2008 IEEE.

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

Projec ted texture for hand geometry based authentication

Symmetry and affine repetitions are common in scenes with man-made structures. In this paper we propose a technique to exploit affine repetitions in a 3D scene for reconstruction and view synthesis from a single image. Assuming three vanishing points in the image, we show how the 3D structure of multiple objects and their affine repetitions may be computed and used for synthesizing new views. The reconstructed objects may also be inserted in other scenes to create augmented images. © 2008 IEEE.

On exploiting affine repetitions for 3D reconstruction from a single image

Automatic fingerprint identification systems use level-1 and level-2 features for fingerprint identification. However, forensic examiners utilize inherent level-3 details along with level-2 features. Existing level-3 feature extraction algorithms are computationally expensive to be used for identification. This paper presents a novel algorithm for fast level- 3 feature extraction and identification. The algorithm starts with computing local image quality score using redundant discrete wavelet transform. A fast curve evolution algorithm is then used to extract four level-3 features namely, pores, ridge contours, dots, and incipient ridges. Along with level-1 and level-2 features, these level-3 features are used in a Delaunay triangulation based indexing algorithm. Finally, quality-based likelihood ratio is used to further improve the identification performance. Experiments conducted on a high resolution fingerprint database containing rolled, slap and latent images indicate that the algorithm offers significant benefits for fast fingerprint identification. © 2008 IEEE.