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.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

The performance of iris recognition is affected if iris is captured at a distance. Further, images captured in visible spectrum are more susceptible to noise than if captured in near infrared spectrum. This research proposes periocular biometrics as an alternative to iris recognition if the iris images are captured at a distance. We propose a novel algorithm to recognize periocular images in visible spectrum and study the effect of capture distance on the performance of periocular biometrics. The performance of the algorithm is evaluated on more than 11,000 images of the UBIRIS v2 database. The results show promise towards using periocular region for recognition when the information is not sufficient for iris recognition. © 2010 IEEE.

World University root node

IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010

Periocular biometrics: When Iris recognition fails

Image Segmentation refers to the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and change the representation of an image into something that is more meaningful and easier to analyze. A Cellular Automata (CA) is a computing model of complex system using simple rule. It divides the problem space into number of cells and each cell can be in one or several final states. Cells are affected by its neighbor's to the simple rule. Learning Cellular Automata (LCA) is a variant of automata that interact with random environment having as goal to improve its behavior. This paper proposes an image segmentation technique based on LCA using soft computing approach. This proposed method works in two steps, the first step is called as soft segmentation where the input image(s) is/are analyzed through LCA and the second step is called as soft computing approach where the analyzed image is segmented through fuzzy C-means algorithm. © 2010 American Institute of Physics.

AIP Conference Proceedings

Image segmentation based on learning cellular automata using soft computing approach

We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a shape from a training data set, followed by a probabilistic label transfer algorithm that is used to match two shapes and to transfer cluster labels from a training-shape to a test-shape. The novelty resides both in the use of the Laplacian embedding to propagate must-link and cannot-link constraints, and in the segmentation algorithm which is based on a learn, align, transfer, and classify paradigm. We compare the results obtained with our method with other constrained spectral clustering methods and we assess its performance based on ground-truth data. © 2010 Springer-Verlag.

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

Learning shape segmentation using constrained spectral clustering and probabilistic label transfer

A hierarchical framework to perform automatic categorization and reorientation of consumer images based on their content is presented. Sometimes the consumer rotates the camera while taking the photographs but the user has to later correct the orientation manually. The present system works in such cases; it first categorizes consumer images in a rotation invariant fashion and then detects their correct orientation. It is designed to be fast, using only low level color and edge features. A recently proposed information theoretic feature selection method is used to find most discriminant subset of features and also to reduce the dimension of feature space. Learning methods are used to categorize and detect the correct orientation of consumer images. Results are presented on a collection of about 7000 consumer images, collected by an independent testing team, from the internet and personal image collections. © 2009 Springer-Verlag Berlin Heidelberg.

Hierarchical system for content based categorization and orientation of consumer images

In this paper, we propose a scheme based on an ontological framework, to recognize concepts in multimedia data, in order to provide effective content-based access to a closed, domain-specific multimedia collection. The ontology for the domain is constructed from high-level knowledge of the domain lying with the domain experts, and further fine-tuned and refined by learning from multimedia data annotated by them. MOWL, a multimedia extension to OWL, is used to encode the concept to media-feature associations in the ontology as well as the uncertainties linked with observation of the perceptual multimedia data. Media feature classifiers help recognize low-level concepts in the videos, but the novelty of our work lies in discovery of high-level concepts in video content using the power of ontological relations between the concepts. This framework is used to provide rich, conceptual annotations to the video database, which can further be used to create hyperlinks in the video collection, to provide an effective video browsing interface to the user. © 2009 Springer-Verlag Berlin Heidelberg.

Using concept recognition to annotate a video collection

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. © 2007 Elsevier B.V. All rights reserved.

Image and Vision Computing

Face recognition with disguise and single gallery images

In large scale applications, hundreds of new subjects may be regularly enrolled in a biometric system. To account for the variations in data distribution caused by these new enrollments, biometric systems require regular re-training which usually results in a very large computational overhead. This paper formally introduces the concept of online learning in biometrics. We demonstrate its application in classifier update algorithms to re-train classifier decision boundaries. Specifically, the algorithm employs online learning technique in a 2ν-Granular Soft Support Vector Machine for rapidly training and updating face recognition systems. The proposed online classifier is used in a face recognition application for classifying genuine and impostor match scores impacted by different covariates. Experiments on a heterogeneous face database of 1,194 subjects show that the proposed online classifier not only improves the verification accuracy but also significantly reduces the computational cost. ©2009 IEEE.

IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009

Online learning in biometrics: A case study in face classifier update

Encyclopedia of Biometrics

Fusion, Sensor Level.

In this paper a new completely unsupervised mesh segmentation algorithm is proposed, which is based on the PCA interpretation of the Laplacian eigenvectors of the mesh and on parametric clustering using Gaussian mixtures. We analyse the geometric properties of these vectors and we devise a practical method that combines single-vector analysis with multiple-vector analysis. We attempt to characterize the projection of the graph onto each one of its eigenvectors based on PCA properties of the eigenvectors. We devise an unsupervised probabilistic method, based on one-dimensional Gaussian mixture modeling with model selection, to reveal the structure of each eigenvector. Based on this structure, we select a subset of eigenvectors among the set of the smallest non-null eigenvectors and we embed the mesh into the isometric space spanned by this selection of eigenvectors. The final clustering is performed via unsupervised classification based on learning a multi-dimensional Gaussian mixture model of the embedded graph. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

AAAI Fall Symposium - Technical Report

Mesh segmentation using Laplacian eigenvectors and Gaussian mixtures

We present a novel predictive statistical framework to improve the performance of an Eigen Tracker which uses fast and efficient eigen space updates to learn new views of the object being tracked on the fly using candid co-variance free incremental PCA. The proposed system detects and tracks an object in the scene by learning the appearance model of the object online motivated by non-traditional uniform norm. It speeds up the tracker many fold by avoiding nonlinear optimization generally used in the literature. © 2009 IEEE.