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

Latent fingerprints recognition is very useful in law enforcement and forensics applications. However, automated matching of latent fingerprints with a gallery of live scan images is very challenging due to several compounding factors such as noisy background, poor ridge structure, and overlapping unstructured noise. In order to efficiently match latent fingerprints, an effective enhancement module is a necessity so that it can facilitate correct minutiae extraction. In this research, we propose a Generative Adversarial Network based latent fingerprint enhancement algorithm to amplify the ridge structure quality. Experiments on two publicly available datasets, IIITD-MOLF and IIITD-MSLFD show that the proposed enhancement algorithm improves the quality of fingerprint images while preserving the ridge structure. Using the enhanced images with standard feature extraction and matching algorithms further boosts latent fingerprint recognition performance. © 2019 IEEE

World University root node

Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

Latent fingerprint enhancement using generative adversarial networks

Deception detection is a pervasive issue in security. It has been widely studied using traditional modalities, such as video, audio and transcripts; however, there has been a lack of investigation in using modalities such as EEG and Gaze data due to the scarcity of a publicly available dataset. In this paper, a new multimodal dataset is presented, which provides data for deception detection by the aid of various modalities, such as video, audio, EEG and gaze data. The dataset explores the cognitive aspect of deception and combines it with vision. The presented dataset is collected in a realistic scenario and has 35 unique subjects providing 325 annotated data points with an even distribution of truth (163) and lie (162). The benefits provided by incorporating multiple modalities for fusion on the proposed dataset is also investigated. It is our assertion that the availability of this dataset will facilitate the development of better deception detection algorithms which are more relevant to real world scenarios. © 2019 IEEE.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Bag-of-lies: A multimodal dataset for deception detection

Presentation attacks can provide unauthorized access to the users and fool face recognition systems for both small scale and large scale applications. Among all the presentation attacks, 2D print and replay attacks are very popular due to their ease and cost-effectiveness in attacking face recognition systems. However, over the years, there are several successful presentation attack detection algorithms developed to detect 2D print and replay attacks. Generally, 2D presentation attacks are detected using the presence or absence of micro patterns which distinguish a real input from an attacked input. However, if a smart attacker digitally 'pre-processes' the image using intensity transforms and then performs 2D presentation attack, differences between real and attacked samples due to the micro-patterns would be minimized. In this paper, for the first time, we show that simple intensity transforms such as Gamma correction, log transform, and brightness control can help an attacker to deceive face presentation attack detection algorithms. Experimental results demonstrate that the smart attacker can increase the error rate of the hand-crafted as well as deep learning based presentation attack detectors. © 2019 IEEE.

Proceedings - 2019 IEEE 5th International Conference on Multimedia Big Data, BigMM 2019

Deceiving face presentation attack detection via image transforms

The paper proposes a novel approach for learning kernel Support Vector Machines (SVM) from large scale data with reduced computation time. The proposed approach, termed as Subclass Reduced Set SVM (SRS-SVM), utilizes the subclass structure of data to effectively estimate the candidate support vector set. Since the candidate support vector set cardinality is only a fraction of the training set cardinality, learning SVM from the former requires less time without significantly changing the decision boundary. SRS-SVM depends on a domain knowledge related input parameter, i.e., number of subclasses. To reduce the domain knowledge dependency and to make the approach less sensitive to the subclass parameter, we extend the proposed SRS-SVM to create a robust and improved hierarchical model termed as the Hierarchical Subclass Reduced Set SVM (HSRS-SVM). Since SRS-SVM and HSRS-SVM splits non-linear optimization problem into multiple (smaller) linear optimization problems, both of them are amenable to parallelization. The effectiveness of the proposed approaches is evaluated on four synthetic and six real-world datasets. The performance is also compared with traditional solver (LibSVM) and state-of-the-art approaches such as divide-and-conquer SVM, FastFood, and LLSVM. The experimental results demonstrate that the proposed approach achieves similar classification accuracies while requiring fewer folds of reduced computation time as compared to existing solvers. We further demonstrate the suitability and improved performance of the proposed HSRS-SVM with deep learning features for face recognition using Labeled Faces in the Wild (LFW) dataset. © 2019

Pattern Recognition

Between-subclass piece-wise linear solutions in large scale kernel SVM learning

Advancements in machine learning and deep learning techniques have led to the development of sophisticated and accurate face recognition systems. However, for the past few years, researchers are exploring the vulnerabilities of these systems towards digital attacks. Creation of digitally altered images has become an easy task with the availability of various image editing tools and mobile application such as Snapchat. Morphing based digital attacks are used to elude and gain the identity of legitimate users by fooling the deep networks. In this research, partial face tampering attack is proposed, where facial regions are replaced or morphed to generate tampered samples. Face verification experiments performed using two state-of-the-art face recognition systems, VGG-Face and OpenFace on the CMU-MultiPIE dataset indicates the vulnerability of these systems towards the attack. Further, a Partial Face Tampering Detection (PFTD) network is proposed for the detection of the proposed attack. The network captures the inconsistencies among the original and tampered images by combining the raw and high-frequency information of the input images for the detection of tampered images. The proposed network surpasses the performance of the existing baseline deep neural networks for tampered image detection. © 2019 IEEE.

Evading face recognition via partial tampering of faces

The ubiquitous use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor conditions. This scenario has necessitated the development of reliable iris recognition algorithms for such an uncontrolled environment. Additionally, iris presentation attacks such as textured contact lens pose a major challenge to current iris recognition systems. Motivated by these, this paper presents two key contributions. First, a new Unconstrained Multi-sensor Iris Presentation Attack (UnMIPA) database is created. It consists of more than 18,000 iris images of subjects wearing textured contact lens and without wearing contact lenses captured in both indoor and outdoor environment using multiple iris sensors. The second contribution of this paper is a novel algorithm, DensePAD, which utilizes DenseNet based convolutional neural network architecture for iris presentation attack detection. In-depth experimental evaluation of this algorithm reveals its superior performance in detecting iris presentation attack images on multiple databases. The performance of the proposed DensePAD algorithm is also evaluated in real-world scenarios of open-set iris presentation attacks which highlights the challenging nature of detecting iris presentation attack images from unseen distributions. © 2019 IEEE.

Detecting textured contact lens in uncontrolled environment using densePAD

Facial attributes can provide rich ancillary information which can be utilized for different applications such as targeted marketing, human computer interaction, and law enforcement. This research focuses on facial attribute prediction using a novel deep learning formulation, termed as R-Codean autoencoder. The paper first presents Cosine similarity based loss function in an autoencoder which is then incorporated into the Euclidean distance based autoencoder to formulate R-Codean. The proposed loss function thus aims to incorporate both magnitude and direction of image vectors during feature learning. Inspired by the utility of shortcut connections in deep models to facilitate learning of optimal parameters, without incurring the problem of vanishing gradient, the proposed formulation is extended to incorporate shortcut connections in the architecture. The proposed R-Codean autoencoder is utilized in facial attribute prediction framework which incorporates patch-based weighting mechanism for assigning higher weights to relevant patches for each attribute. The experimental results on publicly available CelebA and LFWA datasets demonstrate the efficacy of the proposed approach in addressing this challenging problem. © 2018 Elsevier B.V.

Fulltext

Pattern Recognition Letters

Residual Codean Autoencoder for Facial Attribute Analysis

Autonomous driving is an active area of research. An important aspect of this problem is recognizing the gestures made by humans, both inside and outside the vehicle. In this paper, we present the Auto-G database that comprises different hand gestures for autonomous driving. The database encompasses several challenges such as occlusion, low resolution, motion blur, illumination variation, extreme pose variations, along with the presence of multiple gestures within a frame. We also propose an end-to-end pipeline for hand detection and gesture recognition. The proposed pipeline achieves a frame gesture recognition accuracy of 90.23% on the proposed Auto-G database. © 2019 IEEE.

Proceedings - International Conference on Image Processing, ICIP

AUTO-G: Gesture Recognition in the Crowd for Autonomous Vehicl

Predicting if a person is an adult or a minor has several applications such as inspecting underage driving, preventing purchase of alcohol and tobacco by minors, and granting restricted access. The challenging nature of this problem arises due to the complex and unique physiological changes that are observed with age progression. This paper presents a novel deep learning based formulation, termed as Class Specific Mean Autoencoder, to learn the intra-class similarity and extract class-specific features. We propose that the feature of a particular class if brought similar/closer to the mean feature of that class can help in learning class-specific representations. The proposed formulation is applied for the task of adulthood classification which predicts whether the given face image is of an adult or not. Experiments are performed on two large databases and the results show that the proposed algorithm yields higher classification accuracy compared to existing algorithms and a Commercial-Off-The-Shelf system. © 2018 Elsevier B.V.

Are you eligible? Predicting adulthood from face images via Class Specific Mean Autoencoder

In the recent years, convolutional neural networks (CNN) have been extensively employed in various complex computer vision tasks including visual object tracking. In this paper, we study the efficacy of temporal regression with Tikhonov regularization in generic object tracking. Among other major aspects, we propose a different approach to regress in the temporal domain, based on weighted aggregation of distinctive visual features and feature prioritization with entropy estimation in a recursive fashion. We provide a statistics based ensembler approach for integrating the conventionally driven spatial regression results (such as from ECO), and the proposed temporal regression results to accomplish better tracking. Further, we exploit the obligatory dependency of deep architectures on provided visual information, and present an image enhancement filter that helps to boost the performance on popular benchmarks. Our extensive experimentation shows that the proposed weighted aggregation with enhancement filter (WAEF) tracker outperforms the baseline (ECO) in almost all the challenging categories on OTB50 dataset with a cumulative gain of 14.8%. As per the VOT2016 evaluation, the proposed framework offers substantial improvement of 19.04% in occlusion, 27.66% in illumination change, 33.33% in empty, 10% in size change, and 5.28% in average expected overlap. © Springer Nature Switzerland AG 2019.