Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

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

CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly find one pair of human face images, that belong to same subject, embedded in a complex background without selecting any nonface image or impostor pair. The proposed algorithm generates CAPTCHA that offer better human accuracy and lower attack rates compared to existing approaches. © 2012 IEEE.

World University root node

2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012

Face recognition CAPTCHA

One of the important cues in solving crimes and apprehending criminals is matching sketches with digital face images. This paper presents an automated algorithm to extract discriminating information from local regions of both sketches and digital face images. Structural information along with minute details present in local facial regions are encoded using multiscale circular Weber's local descriptor. Further, an evolutionary memetic optimization algorithm is proposed to assign optimal weight to every local facial region to boost the identification performance. Since forensic sketches or digital face images can be of poor quality, a preprocessing technique is used to enhance the quality of images and improve the identification performance. Comprehensive experimental evaluation on different sketch databases show that the proposed algorithm yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems. © 2012 IEEE.

IEEE Transactions on Information Forensics and Security

Memetically optimized MCWLD for matching sketches with digital face images

With the advancements in iris matching and growing number of system deployments, a wide variety of iris cameras are now being manufactured. These cameras differ in manufacturing technology, including image acquisition spectrum and illumination settings. For large scale applications (e.g. UID system in India) where cameras from several vendors are likely to be used for iris enrollment and authentication, iris camera interoperability is an important consideration. The question we address here is: will the matching accuracy differ in matching iris images captured by two different cameras compared to images captured by the same camera? We propose an iris camera classification-based preprocessing framework to address iris interoperability. The camera classification output is used to perform selective iris image enhancement. Experimental results on the IIITD Multi-Sensor Iris database collected locally and the Notre Dame Cross Sensor database show a significant improvement in the cross-camera iris recognition accuracy using the proposed approach. © 2012 IEEE.

On iris camera interoperability

On simultaneous latent fingerprint matching

Tuple Space based Mobile Middleware (TSMM) intends to facilitate coordination of different components (i.e. agents) of supported applications in multiple mobile, unreliable networks, which is a challenging problem. This paper proposes TSMM-specific discovery and communication mechanisms that enable TSMM to provide coordination over multiple networks. The paper also presents an approach of formalizing proposed mechanisms using Mobile UNITY. © 2012 IEEE.

2012 IEEE International Conference on Advanced Networks and Telecommunciations Systems, ANTS 2012

Supporting Tuple Space based Mobile Middleware over unreliable mobile infrastructures: Design and formal specifications

In this paper we present an approach for correcting character recognition errors of an OCR which can recognise Indic Scripts. Suffix tree is used to index the lexicon in lexicographical order to facilitate the probabilistic search. To obtain the best probable match against the mis-recognised string, it is compared with the sub-strings (edges of suffix tree) using similarity measure as weighted Levenshtein distance, where Confusion probabilities of characters (Unicodes) are used as substitution cost, until it exceeds the specified cost k. Retrieved candidates are sorted and selected on the basis of their lowest edit cost. Exploiting this information, the system can correct nonword errors and achieves maximum error rate reduction of 33% over simple character recognition system. © 2011 IEEE.

Proceedings - 2011 3rd National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2011

Probabilistic approach for correction of optically-character-recognized strings using suffix tree

The paper presents a novel approach for event detection in sports videos by topic based graphical model learning. The characteristics features defining various sport events are extracted by contextual grouping of low-level video and audio features using topic modeling. Event detection is performed by learning the structure of context based distribution of characteristic features by CRF based graphical model. Experimental evaluation of the proposed concept is presented on recorded video of Handball and Soccer game. © 2011 IEEE.

Proceedings of the IEEE International Conference on Computer Vision

A hybrid framework for event detection using multi-modal features

We present a novel word image based document indexing scheme by combination of string matching and hashing. The word image representation is defined by string codes obtained by unsupervised learning over graphical primitives. The indexing framework is defined by distance based hashing function which does the object projection to hash space by preserving their distances. We have used edit distance based string matching for defining the hashing function and for approximate nearest neighbor based retrieval. The application of the proposed indexing framework is presented for two document image collections belonging to Devanagari and Bengali script. © 2011 IEEE.

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Document image indexing using edit distance based hashing

We present here an approach for video image segmentation where spatial segmentation is based on rough sets and granular computing and temporal segmentation is done by consecutive frame subtraction. Then the intersection of the temporal segmentation and spatial segmentation for the same frame is analyzed in RGB feature space. The estimated statistics of the intersecting regions is used for the object reconstruction and tracking. © 2011 Springer-Verlag Berlin Heidelberg.

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

Spatiotemporal approach for tracking using rough entropy and frame subtraction

In an operational biometric verification system, changes in biometric data over a period of time can affect the classification accuracy. Online learning has been used for updating the classifier decision boundary. However, this requires labeled data that is only available during new enrolments. This paper presents a biometric classifier update algorithm in which the classifier decision boundary is updated using both labeled enrolment instances and unlabeled probe instances. The proposed co-training online classifier update algorithm is presented as a semi-supervised learning task and is applied to a face verification application. Experiments indicate that the proposed algorithm improves the performance both in terms of classification accuracy and computational time. © 2011 IEEE.