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

This paper proposes a soft-computing based diagnostic tool for analyzing (white matter changes) demyelination due to radiation therapy given to brain tumor cases. The tool exploits the pattern of changes in gray level distribution using a temporal sequence of magnetic resonance (MR) images. Appearance of white matter changes due to demyelination varies from patient to patient. Further, there exists inherent impreciseness in the white matter change patterns. These characteristics make use of fuzzy features well suited for describing image based temporal patterns. Correlation between these temporal patterns and actual onset of demyelination can be captured by fuzzy rules because of the inherent uncertainty associated with changes in gray level pattern in the image and occurrence of the disease. The tool is based on hybrid approach of two popular approaches of genetic algorithm based machine learning (GBML) techniques namely Michigan and Pittsburgh approach. The genetic algorithm (GA) based machine learning tool generates an optimized rule set to indicate positive (P), negative (N) or doubtful (D) cases of demyelination. © 2009 Elsevier B.V. All rights reserved.

World University root node

Applied Soft Computing Journal

Soft-computing based diagnostic tool for analyzing demyelination in magnetic resonance images

This paper presents an efficient algorithm for matching sketches with digital face images. The algorithm extracts discriminating information present in local facial regions at different levels of granularity. Both sketches and digital images are decomposed into multi-resolution pyramid to conserve high frequency information which forms the discriminating facial patterns. Extended uniform circular local binary pattern based descriptors use these patterns to form a unique signature of the face image. Further, for matching, a genetic optimization based approach is proposed to find the optimum weights corresponding to each facial region. The information obtained from different levels of Laplacian pyramid are combined to improve the identification accuracy. Experimental results on sketch-digital image pairs from the CUHK and IIIT-D databases show that the proposed algorithm can provide better identification performance compared to existing algorithms. © 2010 IEEE.

IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010

On matching sketches with digital face images

Existing face recognition systems have demonstrated success in constrained settings with limited variability in illumination, pose, and expression. However, these incremental improvements are not sufficient to transcend the challenging applications such as identifying missing persons or matching individuals with photo ID. These applications require recognition of face images with aging variations and matching digital to scanned photo images. This paper presents a preprocessing framework to enhance the quality of the input scanned and digital face images and minimize the aging differences. Three face recognition algorithms are used to evaluate the efficacy of the proposed framework. Experimental results computed on a digital and scanned database of 310 subjects show that the framework improves the accuracy of all three algorithms by minimizing the quality differences and the variations due to aging. © 2010 IEEE.

Matching digital and scanned face images with age variation

In this paper, we propose a novel image-based completely automated public turing test to tell computers and humans apart (CAPTCHA) that relies on detecting human faces to provide an additional layer of security in web-based services. Face images were selected from the CMU face database and subjected to different types of distortions at different intensity levels to make the automatic face detection very challenging. An extensive experimental study involving 1,100 individuals was undertaken to determine the efficacy of the proposed approach and evaluate the performance of humans compared to computers. We also used two image quality metrics to objectively study the characteristics of the composite CAPTCHA images. Unlike a text-based CAPTCHA, a major benefit of the proposed image-based face detection CAPTCHA is that it does not have any language barriers. In addition, the proposed CAPTCHA can easily be implemented on handheld devices to provide an additional level of security.

International Journal of Multimedia Intelligence and Security

Image-based face detection CAPTCHA for improved security

We present a video coding technique based on texture synthesis via stitching small patches of previous images as done in texture transfer. Each macroblock is identified as a texture or non texture block by means of an edge based criterion. The texture blocks are reconstructed using the luminance values as the control block information to guide the texture transfer algorithm. By modifying the texture transfer algorithm we ensure both spatial as well as temporal consistency. The non texture blocks and the luminance values for texture blocks can be coded by any encoder and thus our method can act as a pre-processor to any video codec e.g. H.264 and add to the compression obtained. © 2010 IEEE.

Proceedings - International Conference on Image Processing, ICIP

Texture mapping based video compression scheme

Smart cameras are important components in any Human Computer Interaction. In any remote surveillance scenario, smart cameras have to take intelligent decisions to select frames of interest to minimize communication and processing overhead. A clustering based change detection algorithm has been implemented in our smart camera system for filtering frames with significant changes. In this paper we propose a platform based framework for implementing clustering based change detection algorithm using HW-SW co-design based methodology. The complete system is implemented on Xilinx XUP Virtex-II Pro FPGA board. The overall algorithm is running on PowerPC405 and some of the blocks which are computationally intensive and more frequently called are implemented as custom IP using VHDL. Total gate count of the design is 2699K. © 2010 IEEE.

ITC 2010 - 2010 International Conference on Recent Trends in Information, Telecommunication, and Computing

Embedded implementation of change detection algorithm for smart camera applications

In large scale deployment of fingerprint recognition systems, especially in Indian environment, there are some challenges involved. Along with sensor noise and poor image quality, presence of scars, warts, and deteriorating ridge/minutiae patterns in fingerprints from rural population affects the data distribution. In other words, quality of fingerprint patterns, particularly belonging to rural Indian population, may differ from standard urban or western population and may be difficult to process. Since there is no study that analyzes fingerprint images in Indian context, this paper presents an analytical study using standard fingerprint image quality assessment tool and fingerprint databases collected from the rural and urban Indian population. On a database of over 0.25 million images, we observed that the worn and damaged patterns lead to poor quality ridges and therefore can affect the performance. Also, region specific causes such as manual labor and Law-sonia Inermis also degrade the quality of fingerprints. © 2010 IEEE.

Emerging Techniques and Challenges for Hand-Based Biometrics, ETCHB 2010

Analyzing fingerprints of Indian population using image quality: A UIDAI case study

We propose a quality-based fusion scheme for improving the recognition accuracy using color iris images characterized by three spectral channels - Red, Green and Blue. In the proposed method, quality scores are employed to select two channels of a color iris image which are fused at the image level using a Redundant Discrete Wavelet Transform (RDWT). The fused image is then used in a score-level fusion framework along with the remaining channel to improve recognition accuracy. Experimental results on a heterogenous color iris database demonstrate the efficacy of the technique when compared against other score-level and image-level fusion methods. The proposed method can potentially benefit the use of color iris images in conjunction with their NIR counterparts. © 2010 IEEE.

Proceedings - International Conference on Pattern Recognition

Quality-based fusion for multichannel iris recognition

Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a large extent thereby posing a great challenge for face recognition algorithms. The contribution of this research is 1) preparing a face database of 900 individuals for plastic surgery, and 2) providing an analytical and experimental underpinning of the effect of plastic surgery on face recognition algorithms. The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance. Therefore, it is imperative to initiate a research effort so that future face recognition systems will be able to address this important problem. © 2010 IEEE.

IEEE Transactions on Information Forensics and Security

Plastic surgery: A new dimension to face recognition

This paper presents a feasibility study to compare the performance of fingerprint recognition on rural and urban Indian population. The analysis shows that rural population is very challenging and existing algorithms/systems are unable to provide acceptable performance. On the other hand, fingerprint recognition algorithms provide comparatively better performance on urban population. The study also shows that poor images quality, worn and damaged patterns and some special characteristics affect the performance of fingerprint recognition.© Springer-Verlag Berlin Heidelberg 2010.