Munish Jindal

Punjab Technical University, Computational Sciences, Faculty Member

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M.E., Ph.D., UGC-NET, GATE
Address: Dr. Munish Jindal (LM-ISTE, IETE Awardee)
Associate Professor and Head,
Department of Computational Sciences,
Maharaja Ranjit Singh Punjab Technical University,
Dabwali Road, Bathinda-151001, Punjab, India

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Princess Nora University

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Jain University

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Papers by Munish Jindal

FER Review Article

by Manmeet K A U R Brar and Munish Jindal

Facial emotion recognition: A comprehensive review, 2024

Facial emotion recognition (FER) represents a significant outcome of the rapid advancements in ar... more Facial emotion recognition (FER) represents a significant outcome of the rapid advancements in artificial intelligence (AI) technology. In today's digital era, the ability to decipher emotions from facial expressions has evolved into a fundamental mode of human interaction and communication. As a result, FER has penetrated diverse domains, including but not limited to medical diagnosis, customer feedback analysis, the automation of automobile driver systems, and the

evaluation of student comprehension. Furthermore, it has matured into a
captivating and dynamic research field, capturing the attention and curiosity of contemporary scholars and scientists. The primary objective of this paper is to provide an exhaustive review of FER systems. Its significance goes beyond
offering a comprehensive resource; it also serves as a valuable guide for emerging researchers in the FER domain. Through a meticulous examination of existing FER systems and methodologies, this review equips them with essential insights and guidance for their future research pursuits. Moreover, this comprehensive review contributes to the expansion of their knowledge base, facilitating a profound understanding of this rapidly evolving field. In a world increasingly dependent on technology for communication and interaction, the study of FER holds a pivotal role in human-computer interaction (HCI). It not only provides valuable insights but also unlocks a multitude of possibilities for future innovations and applications. As we continue to integrate AI and facial emotion recognition into our daily lives, the importance of comprehending and enhancing FER systems becomes increasingly evident. This paper serves as a stepping stone for researchers, nurturing their involvement in this exciting and ever-evolving field.

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Self-supervised learning for medical image analysis: a comprehensive review

Evolving Systems , 2024

Deep learning and advancements in computer vision offer significant potential for analyzing medic... more Deep learning and advancements in computer vision offer significant potential for analyzing medical images resulting in better healthcare and improved patient outcomes. Currently, the dominant approaches in the field of machine learning are supervised learning and transfer learning. These methods are not only prevalent in medicine and healthcare but also across various other industries. They rely on large datasets that have been manually annotated to train increasingly sophisticated models. However, the manual labeling process results in a wealth of untapped, unlabeled data that is accessible in both public and private data repositories. Self-supervised learning (SSL), an emerging field within machine learning, provides a solution by leveraging this untapped, unlabeled data. Unlike traditional machine learning paradigms, SSL algorithms pre-train models using artificial supervisory signals generated from the unlabeled data. This comprehensive review article explores the fundamental concepts, approaches, and advancements in self-supervised learning, with a particular emphasis on medical image datasets and their sources. By summarizing and highlighting the main contributions and findings from the article, this analysis and synthesis aim to shed light on the current state of research in self-supervised learning. Through these rigorous efforts, the existing body of knowledge is synthesized, and implementation recommendations are provided for future researchers interested in harnessing self-supervised learning to develop classification models for medical imaging.

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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems

Engineering Applications of Artificial Intelligence, 2024

Biometric technology, including finger vein, fingerprint, iris, and face recognition, is widely u... more Biometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric systems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with presentation attacks (PAs) being prevalent and easily executed. PAs involve displaying videos, images, or full-face masks to trick biometric systems and gain unauthorized access. Many authors are currently focusing on detecting these presentation attacks (PAD) and have developed several methods, particularly those based on deep learning (DL), which have shown superior performance compared to other techniques. This survey article focuses on manuscripts related to deep learning presentation attack detection, spoof attack detection using deep learning, and anti-spoofing deep learning methods for biometric finger vein, fingerprint, iris, and face recognition. The studies were primarily sourced from four digital research libraries: ACM, Science Direct, Springer, and IEEE Xplore. The article presents a comprehensive review of DL-based PAD systems, examining recent literature on DL-based PAD methods in finger vein, fingerprint, iris, and face detection systems. Through extensive research of the literature, recent algorithms and their solutions for relevant PAD approaches are thoroughly analyzed. Additionally, the article provides a performance analysis and highlights the most promising research findings. The discussion section addresses current issues, opportunities for advancement, and potential solutions associated with deep learning-based PAD methods. This study is valuable to various community users seeking to understand the significance of this technology and its recent applicability in the development of biometric technology for deep learning.

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Age, gender and handedness prediction using handwritten text: A comprehensive survey

Engineering Applications of Artificial Intelligence

Handwriting recognition is widely acknowledged as one of the most challenging tasks in pattern re... more Handwriting recognition is widely acknowledged as one of the most challenging tasks in pattern recognition and machine learning. This study explores the intriguing field of age, gender and handedness prediction through handwriting analysis by offering valuable insights into its potential applications in forensics, psychology and education. A comprehensive survey has been conducted encompassing an introduction, motivational aspects, background information, sources of data, analysis schemes, survey protocols, reported findings and methodologies used for predicting age, gender and handedness in both Indic and non-Indic scripts. The investigation highlights research gaps and concludes with an in-depth analysis of unresolved issues accompanied by a roadmap for future advancements in this domain. The survey focuses on a systematic examination of eight Indic scripts (Assamese, Bangla, Devanagari, Gurumukhi, Kannada, Malayalam, Tamil and Telugu) and six non-Indic scripts (Arabic, Chinese, Japanese, Persian, Roman and Thai) and it can be concluded that non-Indic scripts attain more accuracy in comparison with Indic scripts. Furthermore, the study mostly focuses on providing a catalog of publicly accessible online datasets featuring diverse handwriting samples from various scripting languages by giving a roadmap for further research in this area.

Sentiment analysis of Hindi language text: a critical review

Multimedia Tools and Applications

Sentiment analysis involves extracting sentiments from various forms of text, including customer ... more Sentiment analysis involves extracting sentiments from various forms of text, including customer reviews, tweets, blogs, and news clips expressing opinions on diverse subjects, even populist events. The advent of tools supporting regional languages has resulted in a substantial surge of regional language texts. As Hindi ranks fourth in terms of native speakers, the development of sentiment analysis mechanisms for Hindi text becomes crucial. This paper provides a comprehensive review of specific approaches used in Hindi sentiment analysis, encompassing negation handling and the evolution of SentiWordNet for the Hindi Language. Moreover, it offers an overview of available Hindi lexicons and insights into diverse stemmers and morphological analyzers designed for the language. Additionally, the paper conducts an in-depth literature review of various sentiment analysis tasks carried out in Hindi, thereby opening avenues for future research in sentiment analysis and opinion mining in the Hindi language.

Gender Classification from Offline Handwriting Images in Urdu Script: LeNet-5 and Alex-Net

2023 3rd International Conference on Applied Artificial Intelligence (ICAPAI)

Everyone has a distinct handwriting style, even identical twins who share the same genes. This di... more Everyone has a distinct handwriting style, even identical twins who share the same genes. This distinctiveness has allowed researchers to develop systems that can classify the writer&#39;s traits, including gender, age, handedness, and others. A gender classification system, for instance, classifies handwriting patterns to predict the writer&#39;s gender. With the advancement of deep learning, it is now possible to automatically extract features from the document and classify them. In this paper, two gender classification models based on Alex-Net and LeNet-5 CNN were proposed, trained, and tested on a private Urdu handwriting dataset containing 284,000 pre-segmented characters from 200 males and 200 females. The proposed models achieved state-of- the-art performance compared to existing deep learning models for gender classification. The Alex-Net model achieved an overall accuracy of 99.14%, while the LeNet-5 model achieved an overall accuracy of 98.55%.

Handwriting-based gender classification using machine learning techniques

Multimedia Tools and Applications

Biometrics encloses the science of measuring human body characteristics and authorizing the user ... more Biometrics encloses the science of measuring human body characteristics and authorizing the user based on biometric modalities. Physiological and behavioural biometrics identifiers are the two kinds of biometrics. The present article i.e. Gender Classification system (GCS) is one of the most challenging and serviceable application among the many behavioural biometric based artificial intelligence and machine learning systems. In the current experiment, handwriting modality has been in practice for the gender classification. For the experimental evaluation, a corpus consisting of 200 writers with offline handwriting samples from 100 males and 100 females in Gurumukhi script has been pre-processed using pre-processing algorithms, followed by extracting features by exploiting Zoning, Diagonal, Peak extent, Transition and hybridization of feature extraction algorithms. Based on the extracted features, gender is classified using classification techniques, namely, K-NN, Decision trees, Random Forest, and Adaptive boosting methodology. Performance of the experiment has been analysed using evaluation metrics such as classification accuracy, precision rate, area under the curve, root mean square error and false-positive rate. The proposed system achieves maximum accuracy of 94.6% for gender classification using hybridization of features based on offline handwriting in Gurumukhi script.

Bagging: An Ensemble Approach for Recognition of Handwritten Place Names in Gurumukhi Script

ACM Transactions on Asian and Low-Resource Language Information Processing

In this article, the authors present an effort to recognize handwritten Gurumukhi place names for... more In this article, the authors present an effort to recognize handwritten Gurumukhi place names for use in postal automation. Five feature extraction techniques (zoning, horizontal peak extent, vertical peak extent, diagonal, and centroid) have been analyzed and optimized using Principal Component Analysis (PCA). Four classification methods ( k -Nearest Neighbor ( k -NN), decision tree, random forest, and Convolutional Neural Network (CNN)) have been utilized to classify the handwritten word images. To enhance the recognition results, the authors have employed Bootstrap Aggregation (Bagging) with a majority voting scheme. The authors used a public benchmark dataset of 40,000 handwritten place-name samples in the Punjabi language for their experimental work. The experiments were conducted using a 70:30 partitioning approach, where 70% of the data was utilized for training and the remaining 30% for testing. The system achieved a maximum recognition accuracy of 96.98% by utilizing a comb...

DeepNet-Gait: Human Identification by Gait Using Convolutional Neural Network Model

2023 10th International Conference on Signal Processing and Integrated Networks (SPIN)

Worddeepnet: handwritten gurumukhi word recognition using convolutional neural network

Multimedia Tools and Applications

Prediction of the mortality rate and framework for remote monitoring of pregnant women based on IoT

Multimedia Tools and Applications

To save the risk during the pregnancies in remote areas (where women cannot approach the doctors ... more To save the risk during the pregnancies in remote areas (where women cannot approach the doctors in the urban areas for proper check-ups), the authors have proposed an IoT based remote monitoring of pregnant ladies where the data is collected at the cloud server. Machine learning techniques will be applied on the trimesters’ attributes to find out the reasons of mortality rate of the babies. The use of these advanced technologies in pregnant women care environment can absolutely eradicate the pregnancy complications and harmful incidents. An initial work towards this study is to assess mortality risk prediction in pregnant ladies using machine-learning algorithms for proper prediction and treatment on time. A dataset of 10,000 pregnant women is analysed in this study. Classification algorithms are used to check the death rate of new born babies based on the mother’s age. The survival ratio is presented by applying the various algorithms. Two class SVM model is presented as the most accurate prediction model which outperformed over boosted decision tree, average perceptron, decision forest, locally deep SVM, Bayes point machine, decision forest and logistic regression. Age of the mother and frequency of the pregnancy without proper gap (i.e. less than 3 years) make impact on the mortality rate of the babies. Among the various algorithms, the AUC value of decision forest is augmented at 91.4% whereas two class SVM shows significantly improved performance to 96.6%. The authors have proposed machine learning-based final model, death rate of infants, frequency of pregnancy and age of the mother were interrelated as notable risk factors for mortality in Indian ladies along with other issues.

Analytical Study of Hybrid Features and Classifiers for Cattle Identification

Cognitive science and technology, 2023

Semantic segmentation of palpebral conjunctiva using predefined deep neural architectures for anemia detection

Procedia Computer Science

Non-invasive detection of anemia is generally done by physical examination of regions like palpeb... more Non-invasive detection of anemia is generally done by physical examination of regions like palpebral conjunctiva, fingernails, tongue and palmar creases. However, such examination is subject to large inter-and intra-observer bias. This problem can be alleviated by automating the anemia detection process through computerized analysis of images of these regions. The automated process includes sub-processes like preprocessing, segmentation of region-of-interest (ROI), ROI analysis or feature extraction, and classification. Of all these sub-processes, segmentation is the most crucial one as it helps in the precise extraction of ROI where the most crucial information for decision making lies. Recently, deep learning-based architectures have given exemplary performance in biomedical image segmentation. This paper is unique in a sense that it simulatneously analyzes performance of five deep learning-based architectures namely UNet, UNet++, FCN, PSPNet, and LinkNet. The experiments are performed on customly built dataset comprising of 2592 palpebral images of the pediatric population. The experimental results indicate that as compared to its counterparts, the LinkNet architecture performs the best. Its scores 94.17%, 90.14% and 93.78% for the accuracy, intersection-over-union (IoU), Dice score performance metrics, respectively. The study concludes that LinkNet architecture can be used for real-time segmentation of palpebral conjunctiva from images.