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

Most of the popular multimedia sharing web-sites such as YouTube, Flickr etc not only allow users to author and upload content but also facilitate 'social' networking amongst users. These social interactions can be in the form of - user-to-user interactions i.e. adding existing users to friend or contact list or user-to-content interactions: commenting on a video or picture, marking a picture/video as 'favorite', subscribing to a user created 'channel' etc. Analyzing these social interactions jointly with the content metadata (such as the description of the video, keywords associated with the image/video etc) can reveal interesting insights about user activity on these social media platforms. In this paper, we propose an unsupervised method that jointly models 'social' interaction and content metadata in YouTube to discover user-communities and the nature of topics beings discussed in these communities. We report the effectiveness of the proposed method on real-world dataset. © 2014 IEEE.

World University root node

Proceedings - International Conference on Pattern Recognition

Discovering user-communities and associated topics from YouTube

Due to widespread applications, availability of large intra-personal variations in video and limited information content in still images, video-based face recognition has gained significant attention. Unlike still face images, videos provide abundant information that can be leveraged to address variations in pose, illumination, and expression as well as enhance the face recognition performance. This paper presents a video-based face recognition algorithm that computes a discriminative video signature as an ordered list of still face images from a large dictionary. A three-stage approach is proposed for optimizing ranked lists across multiple video frames and fusing them into a single composite ordered list to compute the video signature. This signature embeds diverse intra-personal variations and facilitates in matching two videos with large variations. For matching two videos, a discounted cumulative gain measure is utilized, which uses the ranking of images in the video signature as well as the usefulness of images in characterizing the individual in the video. The efficacy of the proposed algorithm is evaluated under different video-based face recognition scenarios such as matching still face images with videos and matching videos with videos. The efficacy of the proposed algorithm is demonstrated on the YouTube faces database and the MBGC v2 video challenge database that comprise different types of video-based face recognition challenges such as matching still face images with videos and matching videos with videos. Performance comparison with the benchmark results on both the databases and a commercial face recognition system shows the efficiency of the proposed algorithm for video-based face recognition. © 2014 IEEE.

IEEE Transactions on Information Forensics and Security

On recognizing faces in videos using clustering-based re-ranking and fusion

Face verification, though an easy task for humans, is a long-standing open research area. This is largely due to the challenging covariates, such as disguise and aging, which make it very hard to accurately verify the identity of a person. This paper investigates human and machine performance for recognizing/verifying disguised faces. Performance is also evaluated under familiarity and match/mismatch with the ethnicity of observers. The findings of this study are used to develop an automated algorithm to verify the faces presented under disguise variations. We use automatically localized feature descriptors which can identify disguised face patches and account for this information to achieve improved matching accuracy. The performance of the proposed algorithm is evaluated on the IIIT-Delhi Disguise database that contains images pertaining to 75 subjects with different kinds of disguise variations. The experiments suggest that the proposed algorithm can outperform a popular commercial system and evaluates them against humans in matching disguised face images. © 2014 Dhamecha et al.

Fulltext

PLoS ONE

Recognizing disguised faces: Human and machine evaluation

Screening mammography has been successful in early detection of breast cancer, which has been one of the leading causes of death for women worldwide. Among commonly detected symptoms on mammograms, mass detection is a challenging problem as the task is affected by high complexity of breast tissues, the presence of pectoral muscles as well as varying shape and size of masses. In this research, a novel framework is proposed which automatically detects mass(es) from mammogram(s) even in the presence of pectoral muscles. The framework uses saliency based segmentation which does not require removal of pectoral muscles, if present. From segmented regions, different features are extracted followed by Support Vector Machine classification for mass detection. The experiments are performed using an existing experimental protocol on the MIAS database and the results show that the proposed framework with saliency based region segmentation outperforms the state-of-art algorithms. © 2013 Elsevier B.V.

Signal Processing

Saliency based mass detection from screening mammograms

The presence of a contact lens, particularly a textured cosmetic lens, poses a challenge to iris recognition as it obfuscates the natural iris patterns. The main contribution of this paper is to present an in-depth analysis of the effect of contact lenses on iris recognition. Two databases, namely, the IIIT-D Iris Contact Lens database and the ND-Contact Lens database, are prepared to analyze the variations caused due to contact lenses. We also present a novel lens detection algorithm that can be used to reduce the effect of contact lenses. The proposed approach outperforms other lens detection algorithms on the two databases and shows improved iris recognition performance. © 2005-2012 IEEE.

Unraveling the effect of textured contact lenses on iris recognition

With the growing need for secure authentication, there is an increasing interest in establishing newer biometric modalities that are verifiable in a fast manner with as few associated complexities as possible. In this research, we propose a new biometric modality using a Leap Motion device. The Leap signature is created by an individual in three-dimensional space in absence of any feedback from objects or surfaces. The proposed framework combines an adaptation of 3D Histogram of Oriented Optical Flow and a new feature descriptor, termed as Histogram of Oriented Trajectories. Experiments are performed on the IIITD Leap Signature Database, which consists of 900 samples from 60 subjects. The results are combined with a four-patch local binary pattern based face verification algorithm. An accuracy of over 91% is achieved on this database, with rate of successful spoofing attempts being approximately 1.4%. © 2014 IEEE.

2014 IEEE International Conference on Image Processing, ICIP 2014

Leap signature recognition using HOOF and HOT features

Face recognition algorithms generally utilize 2D images for feature extraction and matching. To achieve higher resilience toward covariates, such as expression, illumination, and pose, 3D face recognition algorithms are developed. While it is challenging to use specialized 3D sensors due to high cost, RGB-D images can be captured by low-cost sensors such as Kinect. This research introduces a novel face recognition algorithm using RGB-D images. The proposed algorithm computes a descriptor based on the entropy of RGB-D faces along with the saliency feature obtained from a 2D face. Geometric facial attributes are also extracted from the depth image and face recognition is performed by fusing both the descriptor and attribute match scores. The experimental results indicate that the proposed algorithm achieves high face recognition accuracy on RGB-D images obtained using Kinect compared with existing 2D and 3D approaches. © 2014 IEEE.

RGB-D face recognition with texture and attribute features

Coherent adaptive subcarrier modulation (CASM) and Interplex can be seen as particular cases of the QPSK modulation or as an evolution of the conventional constant-envelope subcarrier modulation. Interplex uses exclusively additive methods to multiplex the signals, while CASM employs a combination of angle modulation and additive methods. Binary offset carrier (BOC) and Binary phase shift keying (BPSK) are the suitable candidate navigation purpose. In this paper we are proposing a novel method for combining these two modulation scheme in order to get constant modulation envelope i.e., the total transmitted power does not vary over time, so that the transmitted information is not contained in the signal amplitude and the transmitted signal amplitude becomes less critical. © 2014 IEEE.

2014 International Conference on Signal Processing and Integrated Networks, SPIN 2014

Design and implementation of a novel digital interplex modulator for future navigation satellite system

The use of cascade network consisting of RBF nodes and least square error minimization block to Compressed Sensing for recovery of sparse signals is explored in this paper to improve the computation time and convergence. The proposed algorithm Radial basis function cascade network for Sparse signal Recovery (RASR) uses the L0 norm optimization, L2 least square method and feedback network model to improve the signal recovery performance and computational time over the existing ANN based CSIANN and relaxation based SL0 algorithms. The simulation results and experimental evluation of algorithm performance are presented here. © 2014 IEEE.

2014 20th National Conference on Communications, NCC 2014

Radial basis function cascade network for Sparse signal Recovery (RASR)

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components - facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario. Copyright: © 2014 Yadav et al.