Department of Computer Science & Engineering

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IIT Jodhpur

In the field of digital communication, there has always been a requirement for an efficient, low complex, and high-speed error control encoder and decoder. Many such encoders and decoders for different error control codes have been proposed in the literature by researchers. However, developing such CODECs whose performance can be suitable for the requirements of modern communication systems is still an open research problem. In this paper, one such decoder, namely fast Chase decoder proposed in the literature, has been studied. The hardware design of the decoder has been done and verified with results from MATLAB simulations. An attempt has been made to improve the speed by replacing the ripple carry adder in the design with a fast adder. The hardware architecture is implemented in Xilinx XC7A35T platform, and an increase in computation speed of 5% has been achieved. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

Lecture Notes in Electrical Engineering

Design of High-Speed Turbo Product Code Decoder

This paper presents MagnaSense, the first of its kind wireless computer 'mouse' that uses two magnetometers, and principles from magnetism and electromagnetic induction to achieve performance comparable to a traditional computer 'mouse', but with three degrees of freedom instead of two. We demonstrate MagnaSense to have the ability to draw various shapes as well as write text on the screen. Our prototype of MagnaSense is affordable, non-intrusive, handy and easy to use, with the user just requiring a magnet in his hand. © 2021 IEEE.

2021 International Conference on COMmunication Systems and NETworkS, COMSNETS 2021

MagnaSense: The Flying Mouse

Sketch to digital image matching refers to the problem of matching a sketch image (often drawn by hand or created by a software) against a gallery of digital images (captured via an acquisition device such as a digital camera). Automated sketch to digital image matching has applicability in several day to day tasks such as similar object image retrieval, forensic sketch matching in law enforcement scenarios, or profile linking using caricature face images on social media. As opposed to the digital images, sketch images are generally edge-drawings containing limited (or no) textural or colour based information. Further, there is no single technique for sketch generation, which often results in varying artistic or software styles, along with the interpretation bias of the individual creating the sketch. Beyond the variations observed across the two domains (sketch and digital image), automated sketch to digital image matching is further marred by the challenge of limited training data and wide intra-class variability. In order to address the above problems, this research proposes a novel Discriminative Shared Transform Learning (DSTL) algorithm for sketch to digital image matching. DSTL learns a shared transform for data belonging to the two domains, while modeling the class variations, resulting in discriminative feature learning. Two models have been presented under the proposed DSTL algorithm: (i) Contractive Model (C-Model) and (ii) Divergent Model (D-Model), which have been formulated with different supervision constraints. Experimental analysis on seven datasets for three case studies of sketch to digital image matching demonstrate the efficacy of the proposed approach, highlighting the importance of each component, its input-agnostic behavior, and improved matching performance. © 2021 Elsevier Ltd

Pattern Recognition

Discriminative shared transform learning for sketch to image matching

arXiv preprint arXiv:2112.

Anatomizing Bias in Facial Analysis

Low resolution fine-grained classification has widespread applicability for applications where data is captured at a distance such as surveillance and mobile photography. While fine-grained classification with high resolution images has received significant attention, limited attention has been given to low resolution images. These images suffer from the inherent challenge of limited information content and the absence of fine details useful for sub-category classification. This results in low inter-class variations across samples of visually similar classes. In order to address these challenges, this research proposes a novel attribute-assisted loss, which utilizes ancillary information to learn discriminative features for classification. The proposed loss function enables a model to learn class-specific discriminative features, while incorporating attribute-level separability. Evaluation is performed on multiple datasets with different models, for four resolutions varying from 32times 32 to 224times 224. Different experiments demonstrate the efficacy of the proposed attribute-assisted loss for low resolution fine-grained classification.

Proceedings of the International Joint Conference on Neural Networks

Enhancing Fine-Grained Classification for Low Resolution Images

Projection algorithms learn a transformation function to project the data from input space to the feature space, with the objective of increasing the inter-class distance. However, increasing the inter-class distance can affect the intra-class distance. Maintaining an optimal inter-class separation among the classes without affecting the intra-class distance of the data distribution is a challenging task. In this paper, inspired by the Coulomb's law of Electrostatics, we propose a new algorithm to compute the equilibrium space of any data distribution where the separation among the classes is optimal. The algorithm further learns the transformation between the input space and equilibrium space to perform classification in the equilibrium space. The performance of the proposed algorithm is evaluated on four publicly available datasets at three different resolutions. It is observed that the proposed algorithm performs well for lowresolution images.

Class Equilibrium using Coulomb's Law

Handbook of Digital Face Manipulation and Detection From DeepFakes to {\ldots}

Facial retouching and alteration detection

Due to the COVID-19 pandemic, wearing face masks has become a mandate in public places worldwide. Face masks occlude a significant portion of the facial region. Additionally, people wear different types of masks, from simple ones to ones with graphics and prints. These pose new challenges to face recognition algorithms. Researchers have recently proposed a few masked face datasets for designing algorithms to overcome the challenges of masked face recognition. However, existing datasets lack the cultural diversity and collection in the unrestricted settings. Country like India with attire diversity, people are not limited to wearing traditional masks but also clothing like a thin cotton printed towel (locally called as ``gamcha''), ``stoles'' and ``handkerchiefs'' to cover their faces. In this paper, we present a novel \textbf{Indian Masked Faces in the Wild (IMFW)} dataset which contains images with variations in pose, illumination, resolution and the variety of masks worn by the subjects. We have also benchmarked the performance of existing face recognition models on the proposed IMFW dataset. Experimental results demonstrate the limitations of existing algorithms in presence of diverse conditions.

2021 IEEE International Conference on Image Processing (ICIP)

Indian Masked Faces in the Wild Dataset

IIIT-Delhi

Facial Analysis under Low Resolution and Disguise Variations

Identifying and mitigating bias in deep learning algorithms has gained significant popularity in the past few years due to its impact on the society. Researchers argue that models trained on balanced datasets with good representation provide equal and unbiased performance across subgroups. However, can seemingly unbiased pre-trained model become biased when input data undergoes certain distortions? For the first time, we attempt to answer this question in the context of face recognition. We provide a systematic analysis to evaluate the performance of four state-of-the-art deep face recognition models in the presence of image distortions across different gender and race subgroups. We have observed that image distortions have a relationship with the performance gap of the model across different subgroups.