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.

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

Real-world datasets consist of data representations (views) from different sources which often provide information complementary to each other. Multi-view learning algorithms aim at exploiting the complementary information present in different views for clustering and classification tasks. Several multi-view clustering methods that aim at partitioning objects into clusters based on multiple representations of the object have been proposed. Almost all of the proposed methods assume that each example appears in all views or at least there is one view containing all examples. In real-world settings this assumption might be too restrictive. Recent work on Partial View Clustering addresses this limitation by proposing a Non-negative Matrix Factorization based approach called PVC. Our work extends the PVC work in two directions. First, the current PVC algorithm is designed specifically for two-view datasets. We extend this algorithm for the k partial-view scenario. Second, we extend our k partial-view algorithm to include view specific graph laplacian regularization. This enables the proposed algorithm to exploit the intrinsic geometry of the data distribution in each view. The proposed method, which is referred to as GPMVC (Graph Regularized Partial Multi-View Clustering), is compared against 7 baseline methods (including PVC) on 5 publicly available text and image datasets. In all settings the proposed GPMVC method outperforms all baselines. For the purpose of reproducibility, we provide access to our code. © 2016 IEEE.

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Proceedings - International Conference on Pattern Recognition

Partial Multi-View Clustering using Graph Regularized NMF

Vertex Bisection Minimization problem (VBMP) consists of partitioning a vertex set V of graph G = (V, E) into two sets B and B′ where ∣B∣ =⌊∣v∣/2⌋ such that vertex width (VW) is minimized where vertex width is defined as the number of vertices in B which are adjacent to at least one vertex in B′. It is an NP-complete problem in general. VBMP has applications in fault tolerance and is related to the complexity of sending messages to processors in interconnection networks via vertex disjoint paths. In this paper, we have proposed a new integer linear programming (ILP) and quadratically constrained quadratic programming (QCQP) formulation for VBMP. Both of them require number of variables and constraints lesser than existing ILPs and QCQP. We have also implemented ILP and obtained optimal results for various classes of graphs. The result of the experiments with the benchmark graphs shows that the proposed model outperforms the state of the art. Moreover, proposed model obtains optimal result for all the benchmark graphs. © 2016, Industrial Research Institute for Automation and Measurements. All rights reserved.

Journal of Automation, Mobile Robotics and Intelligent Systems

A new integer linear programming and quadratically constrained quadratic programming formulation for vertex bisection minimization problem

This article documents our efforts on machine-mind design for realworld, understanding. It includes: a) enumeration of essential macroarchitectural elements and working principle of our machine-mind framework, b) definition of a novel paradigm, the Z*-numbers, for encapsulation of essential objective and subjective elements of real-world information, and c) a detailed example illustrating the dynamics of the framework and the role of the Z*-numbers. Our work is based on Minsky's theories of cognition, Zadeh's Z-number philosophy, and human brain processes of comprehension. The ideas herein, envision man-machine symbiosis and aim to contribute to the synthesis of an empathetic artificial mind. Among others, the primary novelty in our design lies in the emphasis on representation of the machine-self and its derivates towards emulation of bespoke real-world comprehension. © 2017 by World Scientific Publishing Co. Pte. Ltd.

Pattern Recognition and Big Data

A machine-mind architecture and Z*-numbers for real-world comprehension

Biometric recognition of newborn babies is an opportunity for the realization of several useful applications, such as improved security against swapping and abduction, accurate census, and effective drug delivery. This paper explores the possibility of using face recognition toward an affordable and friendly biometric modality for newborns. The paper proposes an autoencoder-based feature representation followed by problem specific distance metric learning via one-shot similarity with one class-online support vector machine. The largest publicly available database of newborns collected from various sources to study face recognition is introduced. Several existing face recognition approaches and commercial systems are also evaluated on a common benchmark protocol. The efficacy of the proposed algorithm is evaluated under both verification and identification settings. With multiple galleries, rank-1 identification accuracy of 78.5% and verification accuracy of 63.4% at 0.1% false accept rate are achieved. © 2016 IEEE.

IEEE Transactions on Information Forensics and Security

Domain Specific Learning for Newborn Face Recognition

Advancing state of the art in face recognition and bridging the gap between laboratory and real-world scenarios require the availability of challenging databases. One of the challenging applications of face recognition is surveillance, where unconstrained video data is captured both in day and night time (visible and near infrared spectrum). These videos have multiple subjects in each frame, which are matched with good quality gallery images. Due to the lack of an existing database for such a cross spectral cross resolution video-to-still face recognition application, this is still an open research problem. This paper presents a video database that can be utilized to benchmark face recognition algorithms addressing cross spectral cross resolution matching. The proposed Cross-Spectral Cross-Resolution Video dataset (CSCRV) contains videos pertaining to 160 subjects with an open-set protocol. We present baseline results with two commercial matchers for two experimental scenarios, where we observe very low performance of both the matchers. It is our assertion that this dataset can help researchers develop robust face recognition algorithms to handle real world surveillance scenarios. © 2016 IEEE.

2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS 2016

Cross-spectral cross-resolution video database for face recognition

Face Recognition is one of the most extensively researched problems in biometrics, and many techniques have been proposed in the literature. While the performance of automated algorithms is close to perfect in constrained environments with controlled illumination, pose, and expression variations, recognition in unconstrained environments is still difficult. To mitigate the effect of some of these challenges, researchers have proposed to utilize 3D images which can encode much more information about the face than 2D images. However, due to sensor cost, 3D face images are expensive to capture. On the other hand, RGB-D images obtained using consumer-level devices such as the Kinect, which provide pseudo-depth data in addition to a visible spectrum color image, have a trade-off between quality and cost. In this chapter, we discuss existing RGB-D face recognition algorithms and present a state-of-the-art algorithm based on extracting discriminatory features using entropy and saliency from RGB-D images. We also present an overview of available RGB-D face datasets along with experimental results and analysis to understand the various facets of RGB-D face recognition. © Springer International Publishing Switzerland 2016.

Face Recognition Across the Imaging Spectrum

Face recognition with RGB-D images using kinect

Digitally altering, or retouching, face images is a common practice for images on social media, photo sharing websites, and even identification cards when the standards are not strictly enforced. This research demonstrates the effect of digital alterations on the performance of automatic face recognition, and also introduces an algorithm to classify face images as original or retouched with high accuracy. We first introduce two face image databases with unaltered and retouched images. Face recognition experiments performed on these databases show that when a retouched image is matched with its original image or an unaltered gallery image, the identification performance is considerably degraded, with a drop in matching accuracy of up to 25%. However, when images are retouched with the same style, the matching accuracy can be misleadingly high in comparison with matching original images. To detect retouching in face images, a novel supervised deep Boltzmann machine algorithm is proposed. It uses facial parts to learn discriminative features to classify face images as original or retouched. The proposed approach for classifying images as original or retouched yields an accuracy of over 87% on the data sets introduced in this paper and over 99% on three other makeup data sets used by previous researchers. This is a substantial increase in accuracy over the previous state-of-the-art algorithm, which has shown &lt;50% accuracy in classifying original and retouched images from the ND-IIITD retouched faces database. © 2016 IEEE.

Detecting Facial Retouching Using Supervised Deep Learning

Face spoofing can be performed in a variety of ways such as replay attack, print attack, and mask attack to deceive an automated recognition algorithm. To mitigate the effect of spoofing attempts, face anti-spoofing approaches aim to distinguish between genuine samples and spoofed samples. The focus of this paper is to detect spoofing attempts via Haralick texture features. The proposed algorithm extracts block-wise Haralick texture features from redundant discrete wavelet transformed frames obtained from a video. Dimensionality of the feature vector is reduced using principal component analysis and two class classification is performed using support vector machine. Results on the 3DMAD database show that the proposed algorithm achieves state-of-the-art results for both frame-based and video-based approaches, including 100% accuracy on video-based spoofing detection. Further, the results are reported on existing benchmark databases on which the proposed feature extraction framework archives state-of-the-art performance. © 2016 IEEE.

Face anti-spoofing using Haralick features

Person recognition is a challenging research problem particularly if the images are captured at a distance and only ocular region is present. In this research, we present a framework that extracts multiple features from iris and periocular regions from near infrared images captured at a distance of 2 meters or more. Using these features and random decision forest, fusion and classification is performed and verification results are reported. On CASIA V4-at-a-distance and FOCS databases, the proposed algorithm yields state-of-the-art results; particularly achieving over 61% genuine accept rate at 0.1% false accept rate on complete CASIA V4-at-a-distance database. © 2016 IEEE.

Proceedings - International Conference on Image Processing, ICIP

At-a-distance person recognition via combining ocular features

Face identification from low quality and low resolution Near-Infrared (NIR) face images is a challenging problem. Since surveillance cameras typically acquire images at a large standoff distance, the effective resolution of the face is not large enough to identify the individuals. Moreover for a 24-hour surveillance footage, images in low light and at nighttime are acquired in NIR mode which makes the identification problem even more challenging. We propose an effective method using both hand-crafted and learned features for face identification of low resolution NIR images. We show that learned features contribute considerably to the performance of identification algorithm, and that using both feature level and score level fusion in a hierarchal approach gives good performance. The results demonstrate the effectiveness of the proposed approach on images which are of low quality, low resolution and acquired under challenging illumination conditions in near-infrared mode by surveillance cameras. © 2016 IEEE.