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

In this paper, we propose to synthesize natural images from a set of input objects. The proposed technique generates a scene which has high correlation with the provided set of input objects while also maintaining the natural placement of objects within the scene. The technique constitutes of a generative adversarial network trained on a large corpus of objects and natural scenes. This is in contrast with earlier works where the objective was to generate a natural scene from a noise vector or conditioning the network over a variable. However, such methods have limitations in their ability to control the objects within the generated images. On the contrary, we show that by training a Generative Adversarial Network with raw image pixels as input, we can generate scenes which constitute the objects as well as generate the surrounding environment suitable for the combination of the input objects. We provide qualitative and quantitative results on challenging MS-COCO dataset to show the effectiveness of the proposed technique. © 2019 IEEE.

World University root node

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Guided compositional generative adversarial networks

Identifying kinship relations has garnered interest due to several applications such as organizing and tagging the enormous amount of videos being uploaded on the Internet. Existing research in kinship verification primarily focuses on kinship prediction with image pairs. In this research, we propose a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm AutoEncoder (SMNAE). This new autoencoder formulation introduces class-specific sparsity in the weight matrix. The proposed three-stage SMNAE based kinship verification framework utilizes the learned spatio-temporal representation in the video frames for verifying kinship in a pair of videos. A new kinship video (KIVI) database of more than 500 individuals with variations due to illumination, pose, occlusion, ethnicity, and expression is collected for this research. It comprises a total of 355 true kin video pairs with over 250 000 still frames. The effectiveness of the proposed framework is demonstrated on the KIVI database and six existing kinship databases. On the KIVI database, SMNAE yields video-based kinship verification accuracy of 83.18% which is at least 3.2% better than existing algorithms. The algorithm is also evaluated on six publicly available kinship databases and compared with best reported results. It is observed that the proposed SMNAE consistently yields best results on all the databases. © 1992-2012 IEEE.

Fulltext

IEEE Transactions on Image Processing

Supervised mixed norm autoencoder for kinship verification in unconstrained videos

Biometric recognition systems have been shown to be susceptible to presentation attacks, the use of an artificial biometric in place of a live biometric sample from a genuine user. Presentation Attack Detection (PAD) is suggested as a solution to this vulnerability. The LivDet-Iris—Iris Liveness Detection Competition started in 2013 strives to showcase the state-of-the- art in presentation attack detection by assessing the software-based iris PAD methods (Part 1), as well as hardware-based iris PAD methods (Part 2) against multiple datasets of spoof and live fingerprint images. These competitions have been open to all institutions, industrial and academic, and competitors which can enter as either anonymous or using the name of their institution. There have been two previous fingerprint competitions through LivDet; 2013 and 2015. LivDet-Iris 2017 is being conducted during 2017. LivDet-Iris has maintained a consistent level of competitors for Part 1: Algorithms throughout the two previous competitions and 2017 competition has begun to garner further interest. © 2019, Springer Nature Switzerland AG.

Advances in Computer Vision and Pattern Recognition

Review of IRIS presentation attack detection competitions

In this paper, we propose the Radial Loss which utilizes category and sub-category labels to learn an order-preserving fine-grained video similarity metric. We propose an end-to-end quadlet-based Convolutional Neural Network (CNN) combined with Long Short-term Memory (LSTM) Unit to model video similarities by learning the pairwise distance relationships between samples in a quadlet generated using the category and sub-category labels. We showcase two novel applications of learning a video similarity metric - (i) fine-grained video retrieval, (ii) fine-grained event detection, along with simultaneous shot boundary detection, and correspondingly show promising results against those of the baselines on two new fine-grained video datasets. © 2019 IEEE.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Radial Loss for Learning Fine-grained Video Similarity Metric

The increasing trend of using online platforms for real estate rent/sale makes automatic retrieval of similar floor plans a key requirement to help architects and buyers alike. Although sketch based image retrieval has been explored in the multimedia community, the problem of hand-drawn floor plan retrieval has been less researched in the past. In this paper, we propose REXplore (Real Estate eXplore), a novel framework that uses sketch based query mode to retrieve corresponding similar floor plan images from a repository using Cyclic Generative Adversarial Networks (Cyclic GAN) for mapping between sketch and image domain. The key contributions of our proposed approach are: (1) a novel sketch based floor plan retrieval framework using an intuitive and convenient sketch query mode; (2) A conjunction of Cyclic GANs and Convolution Neural Networks (CNNs) for the task of hand-drawn floor plan image retrieval. Extensive experimentation and comparison with baseline results authenticates our claim. © 2018 IEEE

Proceedings - 2018 IEEE International Symposium on Multimedia, ISM 2018

REXplore: A sketch based interactive explorer for real estates using building floor plan images

Recently, deep Convolutional Neural Networks (CNNs) have been extensively studied for hyperspectral image classification. It has undergone significant improvement as compared to conventional classification methods. Yet, there are not much studies have been taken on sub-sampled ground truth dataset in CNN. This paper exploits CNN-based method along with multi-scale and dilated convolution with residual connection concepts for hyperspectral image classification on exclusive real time data set. Two raw and one standard full ground truth Pavia University datasets are used to characterize the performance. Out of raw exclusive datasets, one was taken over urban areas of Ahmedabad, India under ISRO-NASA joint initiative for HYperSpectral Imaging (HYSI) programme, and the other was collected using Hypersec VNIR integrated camera of our institute surroundings from the rooftop of the building. © 2019 IEEE.

Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

Multi-Scale Dilated Residual Convolutional Neural Network for Hyperspectral Image Classification

Do very high accuracies of deep networks suggest pride of effective AI or are deep networks prejudiced? Do they suffer from in-group biases (own-race-bias and own-age-bias) and mimic the human behavior? Is in-group specific information being encoded sub-consciously by the deep networks? This research attempts to answer these questions and presents an in-depth analysis of `bias' in deep learning based face recognition systems. This is the first work which decodes if and where bias is encoded for face recognition. Taking cues from cognitive studies, we inspect if deep networks are also affected by social in- and out-group effect. Networks are analyzed for own-race and own-age bias, both of which have been well established in human beings. The sub-conscious behavior of face recognition models is examined to understand if they encode race or age specific features for face recognition. Analysis is performed based on 36 experiments conducted on multiple datasets. Four deep learning networks either trained from scratch or pre-trained on over 10M images are used. Variations across class activation maps and feature visualizations provide novel insights into the functioning of deep learning systems, suggesting behavior similar to humans. It is our belief that a better understanding of state-of-the-art deep learning networks would enable researchers to address the given challenge of bias in AI and develop fairer systems.

arXiv preprint arXiv:1904.

Deep Learning for Face Recognition: Pride or Prejudiced?

IIITD-Delhi

Video-based person re-Identi fication

Presently, vehicles are not only used for moving from one place to another location but also for various purposes, e.g., location tracking, entertainment, environment monitoring, traffic congestion, smart transportation, and related applications. Moreover, it is possible because of information communication technology. Therefore, we need a communication system to establish a connection between two or more objects on the road networks. However, road-side-units and vehicles communicate with each other in a public environment, and vehicles always change their locality. Thus, vehicle users/systems may face data security issues in this architecture. Recently, Xie et al. suggested an authentication method using identity to deal with reliability, conditional privacy, and integrity in the vehicular ad-hoc networks. However, we identify some security flaws (impersonation, modification, man-in-the-middle, and stolen device) in Xie et al.’s scheme. Thus, we propose an energy-efficient and secure communication using batch verification (ESCBV) protocol to address the present problems of the Xie et al.’s method. Further, we present security proofs to ensure that the suggested method resists different attacks. Moreover, we analyze performance outcomes of the proposed approach to compare with other related schemes, and we notice that the ESCBV system performs better in the implementation time, communication overhead, memory requirement, and energy consumption compared to other related protocols. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Networks

ESCBV: energy-efficient and secure communication using batch verification scheme for vehicle users

With the advancement in networking technologies and emerging applications that work on different protocols, the basic usage of networks, i.e., transferring by packets and global connectivity has to be maintained. The Delay Tolerant Networks (DTNs) are characterized by high end-to-end latency, frequent disconnection, and opportunistic communication over unreliable wireless links. In this paper, we designed a dynamic trust management protocol for secure routing optimization in Delay Tolerant Network(DTN) environments in the presence of well-behaved, selfish and malicious nodes. We develop a novel model-based methodology for delay tolerant networks for extreme distances and trust based techniques to ensure secure routing. © 2018 IEEE.