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1 AI-Powered Facial Expression Analysis In Real Time: Unveiling Emotions 1

1 AI-Powered Facial Expression Analysis In Real Time: Unveil...

引用

1st IEEE international conference on Electronics, computing, Communication and Control Technology, ICECCC 2024

作者： Aishwarya, D. Sandeep, Ponduru Akhilesh Varma, Penmatsa V.P.S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371802

This study introduces a novel facial deconstruction method for face emotion identification. After identifying facial landmarks with the IntraFace algorithm, seven regions of interest (ROI) are extracted, representing the principal featuresof the face: the mouth, nose, an area between the eyebrows, right and left eyes, and eyebrows. Next, a variety of local descriptors, such as Local Binary patterns, Completed Local Binary patterns. Local Ternary patterns, and Dynamic Local Ternary patterns, are employed to capture distinctive features. Inputting the feature vector which represents a face image into a multi-class support vector machine completes the recognition process. The suggested method outperforms previous facial deconstruction techniques, according on experimental findings on two publicly available datasets. © 2024 IEEE.

关键词： Local binary pattern

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Categorical Boosting Machine for Tamil Character recognition Using Shape Based Features 7

Categorical Boosting Machine for Tamil Character Recognition...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： T.m, Vishnu Mukundan Nevatia, Isha Mishra, Tusar Kanti School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350304268

Tamil is one of the world's earliest surviving languages, from which modern Indian scripts draw inspiration. However, there has been limited development of Optical Character recognition techniques for Tamil characters. This paper describes a model for Tamil vowels-based Optical Character recognition. The strategy seeks to utilise a CHFR feature extraction technique to extract three distinct feature vectors from the contours of each individual character's centroid. These feature vectors are condensed into a single vector before being sent to the CatBoost classifier for classification. Using the Optuna framework, hyperparameter optimisation and comparative analysis of existing models are performed. The proposed scheme is applied to a dataset of 6,972 handwritten samples evenly divided into 12 classes representing the vowels of the Tamil alphabet. The primary evaluation metric is accuracy, and 84.36% testing accuracy is observed. © 2023 IEEE.

关键词： CatBoost CHFR feature extraction handwritten character recognition OCR pattern recognition Tamil characters

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An Experimental Analysis on Mapping Strategies for Cepstral Coefficients Multi-projection in Voice Spoofing Detection Problem 22nd

An Experimental Analysis on Mapping Strategies for Cepstral ...

引用

22nd international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Contreras, Rodrigo Colnago Viana, Monique Simplicio Guido, Rodrigo Capobianco Sao Paulo State Univ Inst Biosci Letters & Exact Sci BR-15054000 Sao Jose Do Rio Preto SP Brazil Univ Fed Sao Carlos Dept Comp BR-13565905 Sao Carlos SP Brazil

ISBN: (纸本)9783031425073;9783031425080

With the cheapening of certain electronic devices, the use of biometric user authentication systems is becoming increasingly common. In particular, the use of microphones for user recognition is a reality for many people in the most common tasks, such as accessing their own cell phone or even their own bank account at an ATM. However, recent works demonstrate that the practice of frauds such as the improper presentation of a recording of an authentic user to the recognition system can pose a threat to the security of these systems. As a countermeasure to such practices, liveness detection techniques have emerged with the intention of ensuring that the signal presented to the system comes from the human vocal tract or from a recording. Techniques based on the analysis of spectrograms, which consist of a matrix representation of the relationship between the frequency of the signal and its temporal duration, are widely used in this topic. However, little study has been carried out on the use of mapping operators on such representations in order to make feature vectors belonging to Rn and, consequently, facilitate its definition and use in most binary classifiers. In this work, we perform an experimental analysis on different mapping operators applied in the construction of handcrafted features on spectrograms calculated by various techniques. In addition, we also analyze the effect of fusing and projecting such features. Finally, we were able to observe that, with the use of the proposed material, which consists of a tool of simple definition and low complexity, we obtained competitive results to those presented by the baselines of the theme.

关键词： Liveness Detection Spoofing Detection Voice Authentication System Spectrograms pattern recognition

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Facial Emotion recognition Using a Local Binary pattern based Deep Learning 2

Facial Emotion Recognition Using a Local Binary Pattern base...

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2nd international conference on Computer, Communication and Control, IC4 2024

作者： Tayubi, Iftikhar Aslam Pawar, Dilip N. Kiran, Ajmeera Reddy, Pundru Chandra Shaker Sharma, Nipun Chitra, D. King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Bharati Vidyapeeth College of Engineering Instrumentation Engineering Maharashrra Navi Mumbai India Mlr Institute of Technology Dundigal Department of Computer Science and Engineering Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Presidency University Electronics and Communication Engineering Karnataka Bangalore India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Facial-emotion-recognition(FER) is being conducted with the goals of analyzing the psychological characteristics of juvenile offenders and promoting the use of deep learning to the extraction of psychological features. Initially, we talk about how certain personality traits are linked to being good at reading facial expressions. To build a facial emotion identification model from this data, we add more convolutional layers to a CNN and combine it with other neural networks like VGGNet, AlexNet, and LeNet-5. Next, a Central-Local-Binary-pattern(CLBP) method, which is optimized, is incorporated into the CNN to build a CNN-CLBP framework for FER. After face photos have been preprocessed and key parameters have been optimized, the algorithm is subjected to a validity study. The average detection rate for the CNN-CLBP is 88.16%, which is greater than the recognition rate for other approaches. In addition, image preparation and parameter tuning boost the recognition accuracy of this approach without resorting to poor-tting. In addition, the CNN-CLBP system accurately identifies 97% of pleased and startled expressions, but 22.54% of sad ones. The study's findings can be used as a starting point for further investigations into the mental health of teen offenders. © 2024 IEEE.

关键词： Local binary pattern

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computing with Memristor-based Nonlinear Oscillators 22

Computing with Memristor-based Nonlinear Oscillators

引用

IEEE 22nd international conference on Nanotechnology (NANO)

作者： Zoppo, Gianluca Marrone, Francesco Bonnin, Michele Corinto, Fernando Politecn Torino Dept Elect & Telecommunicat Turin Italy

ISBN: (数字)9781665452250

ISBN: (纸本)9781665452250

Among the recent disruptive technologies, volatile/nonvolatile memory-resistor (memristor) has attracted the researchers' attention as a fundamental computation element. It has been experimentally shown that memristive elements can emulate synaptic dynamics and are even capable of supporting spike timing dependent plasticity (STDP), an important adaptation rule for neuromorphic computing systems. The overall goal of this work is to provide an unconventional computing platform exploiting memristor-based nonlinear oscillators described by means of phase deviation equations. Experimental results show that the approach significantly outperforms conventional architectures used for pattern recognition tasks.

关键词： Phased arrays Computational modeling Neural networks Mathematical models Numerical models Timing pattern recognition

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pattern-Driven Privacy: Columnar Binary Transformation for Robust Iris recognition

Pattern-Driven Privacy: Columnar Binary Transformation for R...

引用

2024 international Research conference on Smart computing and Systems Engineering, SCSE 2024

作者： Wickramaarachchi, Wiraj Udara Zhao, Dongdong Zhou, Junwei Xiang, Jianwen Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Rajarata University of Sri Lanka Department of Information and Communication Technology Mihintale Sri Lanka

ISBN: (纸本)9798350375688

Iris is a distinct and strong biometric feature that is being employed as a method of authentication all over the world. Irreversibility, Renewability, and Unlinkability are the most critical aspects required to ensure the privacy of biometrics. Keeping recognition performance at a high level while providing privacy is a critical challenge that many researchers try to overcome. Information distortion through transforming original iris data into another form of information can be used to ensure privacy features. Therefore, this paper proposes a novel approach for iris authentication that ensures privacy features and high recognition performance. The binary representation of the existing iris template is transposed to another new binary representation by computing it column-wise. The Genuine Acceptance Rate (GAR) depicted for the proposed method is 97.11%, while the False Acceptance Rate (FAR) is 0.01%. Compared to the existing research works, the proposed method demonstrates an Equal Error Rate (EER) of 0.59%. The theoretical and experimental proof with explanation is stated in the paper to confirm the privacy aspects. © 2024 IEEE.

关键词： Biometrics

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Motion pattern recognition of Rehabilitation Robot Based on Information Flow Gain Algorithm 14th

Motion Pattern Recognition of Rehabilitation Robot Based on ...

引用

14th international conference on Frontier computing, FC 2024

作者： Qian, Hao Physical Education Teaching and Research Department Xinjiang University Xinjiang Urumqi830046 China

ISBN: (纸本)9789819627936

Many patients who have leg injuries from accidents or who are unable to walk independently are afraid of falling when participating in rehabilitation, which prevents these patients from participating in daily life. To overcome these limitations, the study designed a multidirectional weightlifting support system that can be trained in a safe environment. In addition to walking on the ground, the system can help patients train for more everyday walking tasks in a very realistic environment. Necessary assistance can be provided through task-related support design during user movement. One of the remaining challenges is how to manually switch between AIDS for different tasks. This approach is error-prone and cumbersome, distracting both the therapist and the patient, and interfering with the training workflow. Therefore, in this paper, the information flow gain algorithm is used to optimize the original system of the rehabilitation robot to improve the recognition accuracy of the robot motion pattern. Finally, the experimental results showed that the motion recognition accuracy of 8 subjects wearing rehabilitation equipment reached more than 80%, and the average accuracy of each muscle movement recognition was more than 85%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robots

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2024 IEEE international conference on Consumer Electronics, ICCE 2024

2024 IEEE International Conference on Consumer Electronics, ...

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2024 IEEE international conference on Consumer Electronics, ICCE 2024

ISBN: (纸本)9798350324136

The proceedings contain 361 papers. The topics discussed include: channel selective relation network for efficient few-shot facial expression recognition;a stretchable conductive fiber for wearable strain sensor application;15-ch wearable body surface potential sensor for wireless electrocardiogram monitoring;long-range drone detection dataset;estimation of continuous arterial blood pressure waveform from photoplethysmogram in terms of loss function focus on blood pressure;implementing a dynamic-static hybrid fog computing system for content sharing on local networks;a home brew audio compression scheme that is rate-distortion competitive;soft speech, loud world: bone conduction microphones enhance voice assistant interaction;software development efficiency metrics prediction using fed-layered self-attention grid;and device free wireless sensing based human activity recognition using commercial off-the-shelf IoT single-board computers.

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EB-SNN: An Ensemble Binary Spiking Neural Network for Visual recognition 27th

EB-SNN: An Ensemble Binary Spiking Neural Network for Visua...

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27th international conference on pattern recognition, ICPR 2024

作者： Li, Xinjie Tang, Jianxiong Lai, Jianhuang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangzhou510555 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783031781858

In recent years, spiking neural networks (SNNs) have gained significant attention in visual recognition tasks due to the low computational energy. However, most SNNs have a large number of parameters, which limits their use on resource-limited devices. In this paper, we propose an Ensemble Binary Spiking Neural Network (EB-SNN) for accurate and memory-friendly visual recognition. The EB-SNN is modeled by Ensemble Binary Weights (EBW) module, which integrates multiple binary weights for lightweight SNN modeling. Meanwhile, we propose Knowledge Alignment Strategy to ensure that the EB-SNN can approximate a well-trained SNN for good performance. Experimental results show that the EB-SNN can achieve accuracy of 95.39% on CIFAR10, using 9.3% memory of full-precision SNN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Local binary pattern

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

6th International Conference on Computational Intelligence i...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

ISBN: (纸本)9789819780891

The proceedings contain 45 papers. The special focus in this conference is on Computational Intelligence in pattern recognition. The topics include: Vietnamese Text Summarization Using Transformer Approach;DQN Trader: Reinforcement Learning for Automated Trading;indirect Feature Extraction for Efficient Fingerprint recognition;A Novel and Intelligent Neuroimage Analysis for Tumor Classification with Thermo-Color Segmentation Using VGG-16;a Deep Feature Ensemble Methodology for 2D Biomedical Image Classification;detection of Hardware Trojan in Nanocrossbar-Based Cryptographic Circuit;convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture;performance Comparison of Different Machine Learning Classifiers for Diabetes Prediction;Intelligent Anti-tamper Systems: Safeguarding Critical Infrastructure in the Era of Proliferating Interconnected Devices and IoBT;contrasting Various Cryptographic Algorithms for Storage and Cloud computing Services;A New Technique of Cellular-Automata-Based PUF Design in Nanoscale Crossbar Circuits;CrimeScene2Graph: Generating Scene Graphs from Crime Scene Descriptions Using BERT NER;a Squeeze and Excitation Framework Utilizing ResNet-152 for Alzheimer’s Disease Dementia Classification;Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs);efficient Detection of Fake Twitter Profiles on Social Media Through Hybrid Deep Learning Model;religious Sentiment Analysis and Detection on Social Media;Visualizing NASDAQ Composite Index Historical Data Using Tableau;typeface Identification for English Using Convolutional Neural Networks;Adaptive Ensemble of ML Regressors and LSTM for Stock Price Prediction;PolyNMT: Neural Machine Translation Model with Seq2Seq Encoder-Decoder System;a Rectangular Slotted Sierpinski Fractal Bowtie Antenna for Multiband Wireless Communications;enhancing Cardiac Health: Machine Learning in Coronary Artery Disease Predi

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