&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

Over the years, automatic gender recognition has been used in many applications. However, limited research has been done on analyzing gender recognition across ethnicity scenario. This research aims at studying the performance of discriminant functions including Principal Component Analysis, Linear Discriminant Analysis and Subclass Discriminant Analysis with the availability of limited training database and unseen ethnicity variations. The experiments are performed on a heterogeneous database of 8112 images that includes variations in illumination, expression, minor pose and ethnicity. Contrary to existing literature, the results show that PCA provides comparable but slightly better performance compared to PCA+LDA, PCA+SDA and PCA+SVM. The results also suggest that linear discriminant functions provide good generalization capability even with limited number of training samples, principal components and with cross-ethnicity variations. © 2011 IEEE.

World University root node

Department of Computer Science & Engineering

2011 International Joint Conference on Biometrics, IJCB 2011

Is gender classification across ethnicity feasible using discriminant functions?

Video Data Management and Information Retrieval

Video Biometrics

Simultaneous latent fingerprints are clusters of friction ridge impressions deposited concurrently in a crime scene. The analysis of these impressions is a complex task to infer individualization, exclusion or categorize as inconclusive. The problem is further compounded when distinctive features in each latent fingerprint in the cluster are of varying quality or none of the fingerprint has the requisite number of features to reliably arrive at a conclusion. Recently, SWGFAST (Scientific Working Group on Friction Ridge Analysis, Study and Technology) proposed a draft standard for simultaneous impression examination. The approach is manual and requires known reference ten-print for comparing with an unknown simultaneous latent fingerprint. This paper proposes a semi-automatic approach to process and analyze simultaneous latent fingerprints. The proposed algorithm demonstrates that comparisons can be made from a database of ten-prints for a more comprehensive search instead of the time consuming manual approach used by latent fingerprint examiners. The algorithm was implemented using several soft computing and classification approaches and the performance was compared using the simultaneous latent fingerprint database. The results show that 2ν-SVM with RBF kernel gave the best results both in terms of time and accuracy. © 2010 Elsevier B.V. All rights reserved.

Applied Soft Computing Journal

Simultaneous latent fingerprint recognition

This research presents a forensics application of matching two latent fingerprints. In crime scene settings, it is often required to match multiple latent fingerprints. Unlike matching latent with inked or live fingerprints, this research problem is very challenging and requires proper analysis and attention. The contribution of this paper is three fold: (i) a comparative analysis of existing algorithms is presented for this application, (ii) fusion and context switching frameworks are presented to improve the identification performance, and (iii) a multi-latent fingerprint database is prepared. The experiments highlight the need for improved feature extraction and processing methods and exhibit large scope of improvement in this important research problem. © 2011 IEEE.

On matching latent to latent fingerprints

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

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.

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

On analysis of rural and urban Indian fingerprint images

Design of face detection CAPTCHA for implementing robust security

Newborn swapping and abduction is a global problem and traditional approaches such as ID bracelets and footprinting do not provide the required level of security. This paper introduces the concept of using face recognition for identifying newborns and presents an automatic face recognition algorithm. The proposed multiresolution algorithm extracts Speeded up robust features and local binary patterns from different levels of Gaussian pyramid. The feature descriptors obtained at each Gaussian level are combined using weighted sum rule. On a newborn face database of 34 babies, the proposed algorithm yields rank-1 identification accuracy of 86.9%. © 2010 IEEE.

Face recognition for newborns: A preliminary study

The performance of a large scale biometric system may deteriorate over time as new individuals are continually enrolled. To maintain an acceptable level of performance, the classifier has to be re-trained offline in batch mode using both existing and new data. The process of re-training can be computationally expensive and time consuming. This paper presents a new biometric classifier update algorithm that incrementally re-trains the classifier using online learning and progressively establishes a decision hyperplane for improved classification. The proposed algorithm incorporates soft labels and granular computing in the formulation of a 2ν-Online Granular Soft Support Vector Machine (SVM) to re-train the classifier using only the new data. Granular computing makes it adaptive to local and global variations in data distribution, while soft labels provide resilience to noise. Each time data is acquired, new support vectors that are linearly independent are added and existing support vectors that do not improve the classifier performance are removed. This constrains the size of the support vectors and significantly reduces the training time without compromising the classification accuracy. The efficacy of the proposed online learning strategy is validated in a near infrared face verification application involving different covariates. The results obtained on a heterogeneous near infrared face database of 328 subjects show that in all experiments using different feature extraction and classification algorithms the proposed online 2ν-Granular Soft Support Vector Machine learning approach is 2-3 times faster while achieving a high level of accuracy similar to offline training using all data. © 2010 Elsevier B.V. All rights reserved.

Image and Vision Computing

Biometric classifier update using online learning: A case study in near infrared face verification

Face recognition systems has engrossed much attention and has been applied in various domains, primarily for surveillance, security, access control and law enforcement. In recent years much advancement have been made in face recognition techniques to cater to the challenges such as pose, expression, illumination, aging and disguise. However, due to advances in technology, there are new emerging challenges for which the performance of face recognition systems degrades and plastic/cosmetic surgery is one of them. In this paper we comment on the effect of plastic surgery on face recognition algorithms and various social, ethical and engineering challenges associated with it. © Springer-Verlag Berlin Heidelberg 2010.

Face recognition and plastic surgery: Social, ethical and engineering challenges

Biometric fusion consolidates the output of multiple biometric classifiers to render a decision about the identity of an individual. We consider the problem of designing a fusion scheme when 1) the number of training samples is limited, thereby affecting the use of a purely density-based scheme and the likelihood ratio test statistic; 2) the output of multiple matchers yields conflicting results; and 3) the use of a single fusion rule may not be practical due to the diversity of scenarios encountered in the probe dataset. To address these issues, a dynamic reconciliation scheme for fusion rule selection is proposed. In this regard, the contribution of this paper is two-fold: 1) the design of a sequential fusion technique that uses the likelihood ratio test-statistic in conjunction with a support vector machine classifier to account for errors in the former; and 2) the design of a dynamic selection algorithm that unifies the constituent classifiers and fusion schemes in order to optimize both verification accuracy and computational cost. The case study in multiclassifier face recognition suggests that the proposed algorithm can address the issues listed above. Indeed, it is observed that the proposed method performs well even in the presence of confounding covariate factors thereby indicating its potential for large-scale face recognition. © 2010 IEEE.

Fulltext

On the dynamic selection of biometric fusion algorithms

The performance of iris recognition is affected if iris is captured at a distance. Further, images captured in visible spectrum are more susceptible to noise than if captured in near infrared spectrum. This research proposes periocular biometrics as an alternative to iris recognition if the iris images are captured at a distance. We propose a novel algorithm to recognize periocular images in visible spectrum and study the effect of capture distance on the performance of periocular biometrics. The performance of the algorithm is evaluated on more than 11,000 images of the UBIRIS v2 database. The results show promise towards using periocular region for recognition when the information is not sufficient for iris recognition. © 2010 IEEE.

Periocular biometrics: When Iris recognition fails

This paper presents a face recognition algorithm that addresses two major challenges. The first is when an individual intentionally alters the appearance and features using disguises, and the second is when limited gallery images are available for recognition. The algorithm uses a dynamic neural network architecture to extract the phase features of the face texture using 2D log polar Gabor transform. The phase features are divided into frames which are matched using the Hamming distance. The performance of the proposed algorithm is evaluated using three databases that comprise of real and synthetic face images with different disguise artifacts. The performance of the algorithm is evaluated for decreasing number of gallery images and various types of disguises. In all cases the proposed algorithm shows a better performance compared to other existing algorithms. © 2007 Elsevier B.V. All rights reserved.

Face recognition with disguise and single gallery images

In large scale applications, hundreds of new subjects may be regularly enrolled in a biometric system. To account for the variations in data distribution caused by these new enrollments, biometric systems require regular re-training which usually results in a very large computational overhead. This paper formally introduces the concept of online learning in biometrics. We demonstrate its application in classifier update algorithms to re-train classifier decision boundaries. Specifically, the algorithm employs online learning technique in a 2ν-Granular Soft Support Vector Machine for rapidly training and updating face recognition systems. The proposed online classifier is used in a face recognition application for classifying genuine and impostor match scores impacted by different covariates. Experiments on a heterogeneous face database of 1,194 subjects show that the proposed online classifier not only improves the verification accuracy but also significantly reduces the computational cost. ©2009 IEEE.