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

Latent fingerprint has been used as evidence in the court of law for over 100 years. However, even today, a completely automated latent fingerprint system has not been achieved. Researchers have identified several important challenges in latent fingerprint recognition: 1) low information content; 2) presence of background noise and nonlinear ridge distortion; 3) need for an established scientific procedure for matching latent fingerprints; and 4) lack of publicly available latent fingerprint databases. The process of automatic latent fingerprint matching is divided into five definite stages, and this paper discusses the existing algorithms, limitations, and future research directions in each of the stages. © 2014 IEEE.

World University root node

IEEE Access

Latent fingerprint matching: A survey

Human iris contains rich textural information which serves as the key information for biometric identifications. It is very unique and one of the most accurate biometric modalities. However, spoofing techniques can be used to obfuscate or impersonate identities and increase the risk of false acceptance or false rejection. This paper revisits iris recognition with spoofing attacks and analyzes their effect on the recognition performance. Specifically, print attack with contact lens variations is used as the spoofing mechanism. It is observed that print attack and contact lens, individually and in conjunction, can significantly change the inter-personal and intra-personal distributions and thereby increase the possibility to deceive the iris recognition systems. The paper also presents the IIITD iris spoofing database, which contains over 4800 iris images pertaining to over 100 individuals with variations due to contact lens, sensor, and print attack. Finally, the paper also shows that cost effective descriptor approaches may help in counter-measuring spooking attacks. © 2014 IEEE.

Proceedings - International Conference on Pattern Recognition

On iris spoofing using print attack

In this research work, we introduce a novel approach for phase estimation from noisy reconstructed interference fields in digital holographic interferometry using an unscented Kalman filter. Unlike conventionally used unwrapping algorithms and piecewise polynomial approximation approaches, this paper proposes, for the first time to the best of our knowledge, a signal tracking approach for phase estimation. The state space model derived in this approach is inspired from the Taylor series expansion of the phase function as the process model, and polar to Cartesian conversion as the measurement model. We have characterized our approach bysimulations and validated the performance on experimental data (holograms) recorded under various practical conditions. Our study reveals that the proposed approach, when compared with various phase estimation methods available in the literature, outperforms at lower SNR values (i.e., especially in the range 0-20 dB). It is demonstrated with experimental data as well that the proposed approach is a better choice for estimating rapidly varying phase with high dynamic range and noise. © 2014 Optical Society of America.

Applied Optics

Signal tracking approach for phase estimation in digital holographic interferometry

With data theft and computer break-ins becoming increasingly common, there is a great need for secondary authentication to reduce automated attacks while posing a minimal hindrance to legitimate users. CAPTCHA is one of the possible ways to classify human users and automated scripts. Though text-based CAPTCHAs are used in many applications, they pose a challenge due to language dependency. In this paper, we propose a face image-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly identify visually-distorted human faces embedded in a complex background without selecting any non-human faces. The proposed algorithm generates a CAPTCHA that offers better human accuracy and lower machine attack rates compared to existing approaches. © 2013 Elsevier B.V. All rights reserved.

Future Generation Computer Systems

FaceDCAPTCHA: Face detection based color image CAPTCHA

Encyclopedia of Biometrics

Plastic Surgery and Face Recognition

IEEE 802.11 distributed coordination function supports two access mechanisms - the basic access and the four-way access, both of which are vulnerable to the hidden and the exposed node problem (collectively called the concealed node problem). Though most of the works in literature suggest to overlook this problem due to the associated overhead to solve them, this paper shows that the problem becomes severe for high speed wireless mesh networks. An opportunistic four-way access mechanism is designed to defend this problem in IEEE 802.11n mesh networks that supports high data rates. The performance of the proposed scheme is evaluated in a practical 802.11n indoor mesh testbed, that shows a significant performance improvement compared to the standard access mechanisms. © 2014 IEEE.

2014 6th International Conference on Communication Systems and Networks, COMSNETS 2014

Defending concealedness in IEEE 802.11n

Sink based spanning tree topologies are effectively used in convergecast applications for efficient packet delivery with minimum forwarding delay, eliminating packet loss or duplicate packet delivery. For tree based convergecast, this paper theoretically shows that there is a trade-off between the topology architecture and the network traffic load. Depending on this trade-off, a hierarchical topology adaptation scheme is proposed for the tree based convergecast to improve overall application performances. The effectiveness of the proposed scheme is analyzed using simulation results. Copyright 2014 ACM.

Proceedings of the ACM Symposium on Applied Computing

Hierarchical topology adaptation for distributed convergecast applications

A Cellular Automata (CA) is a computing model of complex System using simple rules. In this paper the problem space is divided into number of cells and each cell can be constituted of one or several final state. Cells are affected by neighbors with the application of simple rule. Cellular Automata are highly parallel and discrete dynamical systems, whose behavior is completely specified in terms of local relation. In this paper CA is applied to solve a bridge traffic control problem. Vehicular travel which demands on the concurrent operations and parallel activities is used to control bridge traffic based on Cellular Automata technique. © Springer International Publishing Switzerland 2014.

Smart Innovation, Systems and Technologies

Parallel Processing Concept Based Vehicular Bridge Traffic Problem

This paper focuses on the recognition and analysis of text embedded in scene images using Deep learning. The proposed approach uses deep learning architectures for automated higher order feature extraction, thereby improving classification accuracies in comparison to handcrafted features used traditionally. Exhaustive experiments have been performed with Deep Belief Networks and Convolutional Deep Neural Networks with varied training algorithms like Contrastive Divergence, De-noising Score Matching and supervised learning algorithms such as logistic regression and Multi-layer perceptron. These algorithms have been validated on 4 standard datasets: Chars 74K English, Chars 74K Kannada, ICDAR 2003 Robust OCR dataset and SVTCHAR dataset. The proposed network achieves improved recognition results on Chars74K English, Kannada and SVTCHAR dataset in comparison to the state-of-art algorithms. For ICDAR 2003 dataset, the proposed network is marginally worse in comparison to Deep Convolutional networks. Although deep belief networks have been considerably used for several applications, according to the knowledge of the authors, this is the first paper to report scene text recognition using deep belief networks.

ACM International Conference Proceeding Series

Scene text analysis using Deep Belief Networks

This paper addresses the challenges in creating good quality composite 3D contents for 3DTV applications and postproduction visual-effects. We present a novel content-aware compositing technique that faithfully preserves the salient structures of cloned source and target content, and avoid major conicting stereopsis cues to maintain a pleasant 3D illusion altogether. Our approach typically learns the appearance layouts of both source and target scene elements. The system extracts object's significance prior maps using classified labels and derive geometric transforms to compensate the 3D perspective mismatches between source and target images using a novel depth image-based rendering procedure. For seamless cloning, we apply a new depth-consistent interpolant technique which utilizes the classified likelihood confidences in weighting the salient or low-significant regions and re-estimating the plausible depth values of the cloned region in accordance with target 3D structure. Further, we adopt a novel content-preserving local warping scheme to reduce the apparent distortions in object shape, size and perspective. Finally, we propose a content-aware mean value cloning technique that seamlessly merges the warped cloned patches with the geometric-appearance context of new background and homogenize vague boundaries with the aid of an object salient map to remove the smudging effects. The overall process is formulated as an energy minimization problem and optimally regularized for large warps, vertical disparities, and stereo baseline changes. Plausible results are demonstrated to show the effectiveness of our approach. Copyright 2014 ACM.