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.

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IIT Jodhpur

In this paper, we propose a novel online multi-camera framework for person identification based on gait recognition using Grassmann Discriminant Analysis. We propose an online method wherein the gait space of individuals are created as they are tracked. The gait space is view invariant and the recognition process is carried out in a distributed manner. We assume that only a fixed known set of people are allowed to enter the area under observation. During the training phase, multi-view data of each individual is collected from each camera in the network and their global gait space is created and stored. During the test phase, as an unknown individual is observed by the network of cameras, simultaneously or sequentially, his/her gait space is created. Grassmann manifold theory is applied for classifying the individual. The gait space of an individual is a point on a Grassmann manifold and distance between two gait spaces is the same as distance between two points on a Grassmann manifold. Person identification is, therefore, carried out on-the-fly based on the uniqueness of gait, using Grassmann discriminant analysis. © 2015, Springer International Publishing Switzerland.

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

Gait recognition based online person identification in a camera network

This paper presents a Deep Bidirectional Long Short Term Memory (LSTM) based Recurrent Neural Network architecture for text recognition. This architecture uses Connectionist Temporal Classification (CTC) for training to learn the labels of an unsegmented sequence with unknown alignment. This work is motivated by the results of Deep Neural Networks for isolated numeral recognition and improved speech recognition using Deep BLSTM based approaches. Deep BLSTM architecture is chosen due to its ability to access long range context, learn sequence alignment and work without the need of segmented data. Due to the use of CTC and forward backward algorithms for alignment of output labels, there are no unicode re-ordering issues, thus no need of lexicon or postprocessing schemes. This is a script independent and segmentation free approach. This system has been implemented for the recognition of unsegmented words of printed Oriya text. This system achieves 4.18% character level error and 12.11% word error rate on printed Oriya text. © 2015 IEEE.

ICAPR 2015 - 2015 8th International Conference on Advances in Pattern Recognition

Text recognition using deep BLSTM networks

A common requirement in camera-based document matching and retrieval systems, is to retrieve a document whose image has been taken under difficult imaging conditions (insufficient and non-uniform illumination, skew, occlusions, all of these possibly coming in together in the same image). We present a system for robust matching and retrieval which works well for such difficult query images, using probabilistic information fusion from multiple independent sources of measurement. Our experiments with two robust and computationally inexpensive features show promising results on a representative database, compared with the state-of-the-art in the area. © 2015 Elsevier B.V. All rights reserved.

Pattern Recognition Letters

Camera-based document image matching using multi-feature probabilistic information fusion

Ubiquitous intelligent devices have enabled provision of smart services to people in seamless way. Context-awareness helps understand current state-of-affairs or the situation in which presently the system is. This understanding helps the IoT application provide more relevant and smarter services based on situations that change over a period of time. In this paper, we propose a novel context-aware situation-tracking framework that makes use of an ontology. The ontology represents the conceptual model of a dynamic world, where situations evolve over time in changing contexts. The ontology provides the reasoning framework to infer about a situation based on the input context data as well as the past information of earlier situations. Future situations can be predicted with some belief based on current situation and incoming context data. The context data is acquired from sensor devices and external inputs. For every recognized situation, system recommends some actions to provide context-aware service. We use Multimedia Web Ontology Language (MOWL) to represents the ontology. MOWL proposes a probabilistic framework for reasoning with uncertainties linked with observation of context. It makes use of Dynamic Bayesian networks to predict and track the dynamically changing situations. We illustrate use of this framework for Smart Mirror use case. © 2015 IEEE.

IEEE World Forum on Internet of Things, WF-IoT 2015 - Proceedings

Ontology based context aware situation tracking

Cross-domain recommendation systems exploit tags, textual descriptions or ratings available for items in one domain to recommend items in multiple domains. Handling unstructured/ unannotated item information is, however, a challenge. Topic modeling offer a popular method for deducing structure in such data corpora. In this paper, we introduce the concept of a common latent semantic space, spanning multiple domains, using topic modeling of semantic clustered vocabularies of distinct domains. The intuition here is to use explicitly-determined semantic relationships between non-identical, but possibly semantically equivalent, words in multiple domain vocabularies, in order to capture relationships across information obtained in distinct domains. The popular WordNet based ontology is used to measure semantic relatedness between textual words. The experimental results shows that there is a marked improvement in the precision of predicting user preferences for items in one domain when given the preferences in another domain. © 2014 IEEE.

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings

Semantic clustering-based cross-domain recommendation

When imaging the heart, using a 2D ultrasound probe, different views can manifest depending on the location and angulations of the probe. Some of these views have been labeled as standard views, due to the presentation and ease of assessment of key cardiac structures in them. We present an approach for automatic recognition and classification of these standard views, as a potential enabler for automated measurements or detection of noise - all without a human in the loop. We present an approach for view classification, Spatial Pyramid Histogram of Words which successfully models the appearance and shape distributions of object class. We demonstrate the effectiveness of this technique for the task of discrimination between the B-mode Parasternal Long Axis (PLAX) and the Short Axis (SAX) echocardiograms. For this task, our method shows a classification accuracy of 98.3% on an exhaustive database of 703 ultrasound images. © 2015 IEEE.

Proceedings - International Symposium on Biomedical Imaging

Echocardiogram view classification with appearance and spatial distributions

Matching near-infrared to visible images is one of the heterogeneous face recognition challenges in which spectral variations cause changes in the appearance of face images. In this paper, we propose to utilize a keypoint selection approach in the recognition pipeline. The proposed keypoint selection approach is a fast approximation of feature selection approach, yielding two orders of magnitude improvement in computational time while maintaining the recognition performance with respect to feature selection. The keypoint selection approach also enables to visualize the keypoints that are important for recognition. The proposed matching framework yields state-of-the-art approaches results on CASIA NIR-VIS-2.0 dataset. © 2015 IEEE.

2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems, BTAS 2015

Feature and keypoint selection for visible to near-infrared face matching

Authenticating fingerphoto images captured using a smartphone camera, provide a good alternate solution in place of traditional pin or pattern based approaches. There are multiple challenges associated with fingerphoto authentication such as background variations, environmental illumination, estimating finger position, and camera resolution. In this research, we propose a novel ScatNet feature based fingerphoto matching approach. Effective fingerphoto segmentation and enhancement are performed to aid the matching process and to attenuate the effect of capture variations. Further, we propose and create a publicly available smartphone fingerphoto database having three different subsets addressing the challenges of environmental illumination and background, along with their corresponding live scan fingerprints. Experimental results show improved performance across multiple challenges present in the database. © 2015 IEEE.

On smartphone camera based fingerphoto authentication

Sketch recognition is one of the integral components used by law enforcement agencies in solving crime. In recent past, software generated composite sketches are being preferred as they are more consistent and faster to construct than hand drawn sketches. Matching these composite sketches to face photographs is a complex task because the composite sketches are drawn based on the witness description and lack minute details which are present in photographs. This paper presents a novel algorithm for matching composite sketches with photographs using transfer learning with deep learning representation. In the proposed algorithm, first the deep learning architecture based facial representation is learned using large face database of photos and then the representation is updated using small problem-specific training database. Experiments are performed on the extended PRIP database and it is observed that the proposed algorithm outperforms recently proposed approach and a commercial face recognition system. © 2015 IEEE.

Proceedings of 2015 International Conference on Biometrics, ICB 2015

Composite sketch recognition via deep network - A transfer learning approach

Alarm Diffusion Systems(ADS) have complex, exacting and critical requirements. In this work we aim to provide a software architecture perspective towards ADS. We look at both functional and quality requirements for an ADS and also attempt to identify certain quality attributes specific to an ADS and attempted to provide a set of architectural tactics to realise them. We also propose a Reference Architecture for designing such systems. We have provided ample examples to support our inferences and take a deeper look at a case study of the Traffic Collision Avoidance System(TCAS) in aircrafts. © Springer International Publishing Switzerland 2015.