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

Sketch recognition has important law enforcement applications in detecting and apprehending suspects. Compared to hand drawn sketches, software generated composite sketches are faster to create and require lesser skill sets as well as bring consistency in sketch generation. While sketch generation is one side of the problem, recognizing composite sketches with digital images is another side. This paper presents an algorithm to address the second problem, i.e. matching composite sketches with digital images. The proposed algorithm utilizes a SSD based dictionary generated via 50,000 images from the CMU Multi-PIE database. The gallery-probe feature vectors created using SSD dictionary are matched using GentleBoostKO classifier. The results on extended PRIP composite sketch database show the effectiveness of the proposed algorithm. © 2014 IEEE.

World University root node

IJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics

Recognizing composite sketches with digital face images via SSD dictionary

The increasing use of smartphones, tablets, and other mobile devices poses a significant challenge in providing effective online security. CAPTCHAs, tests for distinguishing human and computer users, have traditionally been popular; however, they face particular difficulties in a modern mobile environment because most of them rely on keyboard input and have language dependencies. This paper proposes a novel image-based CAPTCHA that combines the touch-based input methods favored by mobile devices with genetically optimized face detection tests to provide a solution that is simple for humans to solve, ready for worldwide use, and provides a high level of security by being resilient to automated computer attacks. In extensive testing involving over 2600 users and 40000 CAPTCHA tests, fgCAPTCHA demonstrates a very high human success rate while ensuring a 0% attack rate using three well-known face detection algorithms. © 2013 IEEE.

Fulltext

IEEE Access

FgCAPTCHA: Genetically Optimized Face Image CAPTCHA 5

Collision-free navigation is an important problem in autonomous robots. In most of the applications, camera vision techniques using stereo-vision and laser scanners have been used. These techniques are not commercially viable for miniature robots due to size and computational limitations. Optical flow based models using monocular vision have shown promise in biomimetic systems to estimate depth information from a scene. In this paper, we propose an obstacle avoidance algorithm that learns optical flow patterns through an SVM classifier. Experimental results and simulation results are presented to validate our approach. The system can be used for indoors and outdoors without modifying the algorithm. © 2014 IEEE.

2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014

Collision avoidance for a low-cost robot using SVM-based monocular vision

The notion of relative attributes as introduced by Parikh and Grauman (ICCV, 2011) provides an appealing way of comparing two images based on their visual properties (or attributes) such as 'smiling' for face images, 'naturalness' for outdoor images, etc. For learning such attributes, a Ranking SVM based formulation was proposed that uses globally represented pairs of annotated images. In this paper, we extend this idea towards learning relative attributes using local parts that are shared across categories. First, instead of using a global representation, we introduce a part-based representation combining a pair of images that specifically compares corresponding parts. Then, with each part we associate a locally adaptive 'significance-coefficient' that represents its discriminative ability with respect to a particular attribute. For each attribute, the significance-coefficients are learned simultaneously with a max-margin ranking model in an iterative manner. Compared to the baseline method, the new method is shown to achieve significant improvement in relative attribute prediction accuracy. Additionally, it is also shown to improve relative feedback based interactive image search. © 2014 IEEE.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Relative parts: Distinctive parts for learning relative attributes

The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose two new centrality measures, Diffusion Degree for independent cascade model of information diffusion and Maximum Influence Degree. Unlike other existing centrality measures, diffusion degree considers neighbors' contributions in addition to the degree of a node. The measure also works flawlessly with non uniform propagation probability distributions. On the other hand, Maximum Influence Degree provides the maximum theoretically possible influence (Upper Bound) for a node. Extensive experiments are performed with five different real life large scale directed social networks. With independent cascade model, we perform experiments for both uniform and non uniform propagation probabilities. We use Diffusion Degree Heuristic (DiDH) and Maximum Influence Degree Heuristic (MIDH), to find the top k influential individuals. k seeds obtained through these for both the setups show superior influence compared to the seeds obtained by high degree heuristics, degree discount heuristics, different variants of set covering greedy algorithms and Prefix excluding Maximum Influence Arborescence (PMIA) algorithm. The superiority of the proposed method is also found to be statistically significant as per T-test.

Fundamenta Informaticae

Centrality measures, upper bound, and influence maximization in large scale directed social networks

For robust face biometrics, a reliable anti-spoofing approach has become an essential pre-requisite against attacks. While spoofing attacks are possible with any biometric modality, face spoofing attacks are relatively easy which makes facial biometrics especially vulnerable. This paper presents a new framework for face spoofing detection in videos using motion magnification and multifeature evidence aggregation in a win- dowed fashion. Micro- and macro- facial expressions commonly exhibited by subjects are first magnified using Eulerian motion magnification. Next, two feature extraction algorithms, a con- figuration of local binary pattern and motion estimation using histogram of oriented optical flow, are used to encode texture and motion (liveness) properties respectively. Multifeature windowed videolet aggregation of these two orthogonal features, coupled with support vector machine classification provides robustness to different attacks. The proposed approach is evaluated and compared with existing algorithms on publicly available Print Attack, Replay Attack and CASIA-FASD databases. The pro- posed algorithm yields state-of-the-art performance and robust generalizability with low computational complexity.

Face Anti-spoofing via Motion Magnification and Multifeature Videolet Aggregation

Abstract. SIMON is a family of 10 lightweight block ciphers published by Beaulieu et al. from the United States National Security Agency (NSA). A cipher in this family with K-bit key and N-bit block is called SIMONN/K. We present several linear characteristics for reduced-round SIMON32/64 that can be used for a key-recovery attack and extend them further to attack other variants of SIMON. Moreover, we provide results of key recovery analysis using several impossible differential characteristics starting from 14 out of 32 rounds for SIMON32/64 to 22 out of 72 rounds for SIMON128/256. In some cases the presented observations do not directly yield an attack, but provide a basis for further analysis for the specific SIMON variant. Finally, we exploit a connection between linear and differential characteristics for SIMON to construct linear characteristics for different variants of reduced-round SIMON. Our attacks extend to all variants of SIMON covering more rounds compared to any known results using linear cryptanalysis. We present a key recovery attack against SIMON128/256 which covers 35 out of 72 rounds with data complexity 2123. We have implemented our attacks for small scale variants of SIMON and our experiments confirm the theoretical bias presented in this work. © Springer International Publishing Switzerland 2014.

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

Cryptanalysis of SIMON variants with connections

In this paper, we address the problem of Content Based Video Retrieval using a multivariate time series modeling of features. We particularly focus on representing the dynamics of geometric features on the Spatio-Temporal Volume (STV) created from a real world video shot. The STV intrinsically holds the video content by capturing the dynamics of the appearance of the foreground object over time, and hence can be considered as a dynamical system. We have captured the geometric property of the parameterized STV using the Gaussian curvature computed at each point on its surface. The change of Gaussian curvature over time is then modeled as a Linear Dynamical System (LDS). Due to its capability to efficiently model the dynamics of a multivariate signal, Auto Regressive Moving Average (ARMA) model is used to represent the time series data. Parameters of the ARMA model are then used for video content representation. To discriminate between a pair of video shots (time series), we have used the subspace angle between a pair of feature vectors formed using ARMA model parameters. Experiments are done on four publicly available benchmark datasets, shot using a static camera. We present both qualitative and quantitative analysis of our proposed framework. Comparative results with three recent works on video retrieval also show the efficiency of our proposed framework. © 2013, Springer-Verlag London.

International Journal of Multimedia Information Retrieval

Multivariate time series modeling of geometric features of spatio-temporal volumes for content based video retrieval

The paper presents a novel framework for the application of multiple features for handwritten data based identity recognition. Different types of features characterise different facets of the handwriting. We have designed a scheme for multiple feature based identity establishment using multi-kernel learning. A new formulation for multi-kernel learning using genetic algorithm has been presented. The efficacy of the framework using individual and combination of features is demonstrated for Devanagari script input. © 2013 Elsevier B.V. All rights reserved.

Pattern Recognition Letters

Off-line hand written input based identity determination using multi kernel feature combination

This paper presents a novel learning based framework to extract articles from newspaper images using a Fixed-Point Model. The input to the system comprises blocks of text and graphics, obtained using standard image processing techniques. The fixed point model uses contextual information and features of each block to learn the layout of newspaper images and attains a contraction mapping to assign a unique label to every block. We use a hierarchical model which works in two stages. In the first stage, a semantic label (heading, sub-heading, text-blocks, image and caption) is assigned to each segmented block. The labels are then used as input to the next stage to group the related blocks into news articles. Experimental results show the applicability of our algorithm in newspaper labeling and article extraction. © 2014 IEEE.