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3rd international conference on Emerging Electrical Energy, Electronics and computing Technologies 2021

3rd International Conference on Emerging Electrical Energy, ...

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3rd international conference on Emerging Electrical Energy, Electronics and computing Technologies 2021, ICE4CT 2021

The proceedings contain 92 papers. The topics discussed include: transformer health index by prediction artificial neural networks diagnostic techniques;energy efficient ant colony system for packet routing in wireless sensor network;evaluation of two-part rain attenuation model at Ku-band for tropical and equatorial regions;performance of restricted fault and bias differential protection against earth fault on a transformer;design and implementation of an automatic speed control system of vehicles for avoiding road accidents in Bangladesh;real time retinal optic disc segmentation via guided filter and discrete wavelet transform;air plasma sterilizer using a parallel dielectric barrier discharge;design and characterization of screen-printed piezoresistive cantilever for gas sensor application;effect of build parameters on process energy consumption and material usage in fused deposition modelling method;and channels selection for pattern recognition of five fingers motor imagery electroencephalography signals.

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A Fine-grained Human Gait recognition Algorithm Based on Unordered Video Sequences

A Fine-grained Human Gait Recognition Algorithm Based on Uno...

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Information and Communication Technology (ICTech), international conference of

作者： Dongsheng Yang Ying Bai Hongliang Wang Xiaoxing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Gait recognition is an effective and unique biometric recognition pattern, and the performance of the algorithm mainly depends on its ability to extract and distinguish features from spatial temporal sequences. To improve the robustness of fine-grained human gait recognition algorithms, this paper proposes a deep gait recognition algorithm based on unordered sequences. Firstly, this article expands the Casia-C dataset to a new large-scale human gait behavior dataset; Secondly, multi-scale serialized feature extraction is performed on shallow to deep features. The improved Gaitset model enhances the mining of fine-grained sequence level features, while preserving the temporal input independence of the original Gaitset model, achieving fast, flexible, and effective gait recognition tasks. Finally, this article designs and implements the overall process from video capture, human contour segmentation and preprocessing to gait behavior recognition. The experimental results show that the proposed algorithm significantly improves accuracy and robustness in different walking states on the most widely used dataset in the field of gait recognition, CASIA-B (Institute of Automation, Chinese Academy of Sciences) and the expanded CASIA-C dataset.

关键词： Legged locomotion Accuracy Image recognition Video sequences Feature extraction Robustness Behavioral sciences pattern recognition Information and communication technology Gait recognition

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

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

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international Research conference on Smart computing and Systems Engineering (SCSE)

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

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

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.

关键词： Privacy Data privacy Error analysis Databases Authentication Systems engineering and theory Distortion

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Optimizing Beat Management System Using soft Biometrics 12th

Optimizing Beat Management System Using Soft Biometrics

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12th international Advanced computing conference, IACC 2022

作者： Kaushik, N. Raju, Chandrakala G. Aradhya, Sumalatha Joshi, Ishaan Indramma, M. Radhika, K.R. Department of ISE BMS College of Engineering Bangalore India Department of CSE Siddaganga Institute of Technology Tumkur India

ISBN: (纸本)9783031356407

In the Police Department, officials need to carry out regular beats in prescribed areas and monitor their attendance in real time. Marking attendance regularly at appropriate beat location at a prescribed time becomes challenging. The motive of the paper is to implement a Beat Management System using the deep metric learning algorithm for the Police Department to collaborate with the community within the assigned territory. The Deep metric learning algorithm in facial recognition systems does an excellent job in addressing the nonlinearity and scalability in comparison to the conventional algorithms. A novel solution is proposed in the form of an end-to-end application in order to overcome the challenges faced by the Police Department in registering attendance. This serves as a concrete solution to the problem with the amount of data that can be generated in the form of facial images from Police Officers along with the precision-based geolocation tracking. The result obtained shows that the model supports even smaller sample sizes of 4–5 images of everyone. Incremental dataset uses the concept of self- learning in order to train the model on more images captured, analyzed and processed continuously in real time. The system recognizes the person by matching the features with an accuracy of 99.38% for a constrained sample size. © 2023, Springer Nature Switzerland AG.

关键词： Face recognition

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Mean AR-Local Binary pattern For Facial Expressions Classification Based on KNN Classifier

Mean AR-Local Binary Pattern For Facial Expressions Classifi...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Narubonia Srilatha Lokeswara Reddy Computer Science and Engineering JNTUA Ananthapur India Computer Science and Engineering K.S.R.M College of Engineering (Autonomous) Kadapa India

According to its significance, facial expression recognition (FER) has reached an incredibly prominent position in the field of image processing, beginning in the 1990s and continuing to the present day. Face recognition, feature extraction, and face categorization are the three common steps in FER. In contrast to the various FER systems described for recognizing just part of face expressions, the system presented in this study can identify all eight fundamental facial emotions (neutral, pleased, angry, contempt, surprise, sad, fear, and disgust). We employ the Japanese Female Facial Expression (JAFFE) and the Extended Cohn-Kanade (CK+) datasets to verify our results. The presented methods LBP patterns in this work namely, Asymmetric Region LBP(AR-LBP), Mean Asymmetric Region LBP (Mean AR-LBP), We used GLCM feature extractor technique for feature selection. For the given input image, The GLCM calculates different features factors named Contrast, Correlation, and Homogeneity, Entropy, Mean, variance, and dissimilarity which were results to a feature vector. The findings of the given approach, which employed K-Nearest Neighbor (KNN) to categories facial emotions, are then contrasted with those of the standard LBP pattern. The recognition rate utilizing the provided technique is 96% for the CK + data set and 97% for the JAFFE data set, as shown by the testing results.

关键词： Emotion recognition Image recognition Correlation Face recognition Feature extraction Entropy Standards

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Speech-to-Text Conversion and Text Summarization

Speech-to-Text Conversion and Text Summarization

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Technological Innovations and Advance computing (TIACOMP), international conference on

作者： Poorva Agrawal Kashish Sharma Keyur Dhage Isha Sharma Nitin Rakesh Gagandeep Kaur Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

The paper presents a new method based on Wav2Vec2 and Heckling Face Transformers (HFTs) speech-to-text conversion and text summarization in Natural Learning Processes for Chatbot systems. Wav2Vec2, being a stronger model in pattern extraction with the help of self-supervision learnings, performs this job well and not needing any manual annotation. In the parallel way, HFT which is an excellent performance when processing texts all around spectra, does very well in accomplishing text summarization. The model architecure merges both module for these tasks in parallel using Wav2Vec2 for speech recognition and fine-tuning for summaries using HFT. This consequently create unified pipeline which represent a fully fledged NLP model for multi-modal NLP tasks, resulting in the most error free of speech-to-text system and the most advanced text summarization model in the world.

关键词： Technological innovation Tensors Error analysis Face recognition Pipelines Text summarization Production Transformers Text to speech Speech to text

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A Tutorial on Dataset Creation for Sensor-based Human Activity recognition

A Tutorial on Dataset Creation for Sensor-based Human Activi...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Christopher Reining Nilah Ravi Nair Friedrich Niemann Fernando Moya Rueda Gernot A. Fink Chair of Material Handling and Warehousing Pattern Recognition in Embedded Systems Group

Well-documented, large and diverse datasets are important to facilitate supervised and transferable deep learning methods for human activity recognition. The past years have seen the machine learning community strive to achieve best practices in dataset creation for various applications. This contribution is the first to provide a comprehensive tutorial on data creation for sensor-based human activity recognition with firsthand instructions. We introduce the dataset creation process consisting of three phases as a guideline for the community. Each phase explains common challenges researchers may face and suggest solutions. A case study demonstrates how the framework can be used by researchers for holistically planning and critically reflecting their approach of dataset creation.

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FPGA Implementation of Textural Feature Extractor and Distance Based Classifier for Multi Modal Biometric pattern recognition System Using Multi Resolution Approach

FPGA Implementation of Textural Feature Extractor and Distan...

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Pervasive computing and Social Networking (ICPCSN), international conference on

作者： S. Hariprasath M. Santhi T.N. Prabakar V. Koushick Aravind. P T. Gunasekaran Department of ECE Saranathan College of Engineering Trichy Department of ECE Sri Sairam College of Engineering Anekal Bangalore Department of ECE Veltech Institute of Science and Technology Chennai Department of Electrical and computer Engineering Mattu University Ethiopia Electrical and Electronics Engineering Section University of Technology and Applied Sciences Muscat Sultanate of Oman

The design of biometric pattern recognition with better accuracy, high robustness and good permanence is a significant design issue in pattern recognition system. A pattern recognition system in which the biological and physiological traits of the user are employed to authenticate the users is known as biometric pattern recognition system. Unimodal biometric systems do not have the required characteristics like solidity, circumvention and permanence. When noisy data acquired from the sensor is given as input, the uni modal biometrics system's accuracy may reduce. Besides the noisy input, less variation of parameters of input data belonging to same class may be a cause for decrease in accuracy of the system. Several such limitations may be removed by applying input data from multi biometric sources. In this work, the design of a textural feature extractor and the classifier for a bimodal biometric system is described. The bimodal biometric system is constructed by fusion of iris code vector and palmprint code vector at feature level. In iris pattern, using the gray level dependent matrix values obtained from wavelet packet decomposed sub-images, the features are extracted Using gabor kernel, features are extracted from the palmprint pattern. The feature vector is formed by the concatenation of both the features after normalization is employed. By binarisation, the binary feature vector is created Using Hamming Distance and its variant, the classifier produces the classification result. The RTL description of the Hamming Distance Classifier (HDC) is written using VHDL 2008 language, Verilog HDL, and the implementation is performed on Spartan 6 FPGA. With a speed grade of 4, the propagation delay of the critical path is 330.12 ns. The obtained delay is sufficient for implementing the classifier as an off-chip design for real-time implementation.

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User recognition based on Gait pattern via Smartwatch Accelerometer in Unrestricted Environment

User Recognition based on Gait Pattern via Smartwatch Accele...

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international conference on Communication, computing and Digital Systems (C-CODE)

作者： Sheharyar Khan Syed M Adnan Shah Aamir Arsalan Wakeel Ahmed Department of Computer Science University of Engineering and Technology Taxila Taxila Pk Department of Software Engineering Fatima Jinnah Women University Rawalpindi Rawalpindi Pk

Wearable gadgets are becoming a major constituent of our society due to a wide range of applications like financial transactions, unlocking automobiles, tracking health and fitness, and many more. Personal data is usually required to manage these services, however, most commercially available wearables devices either lack a user authentication mechanism or employ knowledge-based authentication like passwords, PINs, or pattern locks. In this growing digital age, it is really difficult for an individual to memorize such a huge number of passwords. In this research, we put forward a technique for recognizing users from their walking patterns recorded in real-life settings using a smartwatch accelerometer sensor. Ensemble classification techniques (bagging and boosting) are used for the purpose of user identification using the selected time domain features. Our proposed method attained the highest classification accuracy of 88.35% which transcends the existing user recognition schemes available in the literature.

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Intensified Biological Signature recognition in the Wild: A Case Study 12

Intensified Biological Signature Recognition in the Wild: A ...

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12th international conference on computing Communication and Networking Technologies, ICCCNT 2021

作者： Gandhi, Vidhyotma Singh, Jaiteg Chitkara Institute of Engineering and Technology Chitkata University Punjab India

ISBN: (纸本)9781728185958

The biological signature recognition seems to be a solved problem, but when these systems are tested in the wild their accuracy collapsed abruptly. A case study of "Face recognition Through Camera (FRTC)" was designed to make a state of art for biological signature recognition in the wild system with minimum data set required to provide the highest accuracy. The FRTC system was designed by integrating the best qualities of modern recognition systems present in recent times (OpenCV and Face API) to intensify the biological signature recognition in the wild. The case study FRTC would be tested with the classroom attendance without prior information to the students. It was expected that FRTC would be more accurate than OpenCV and Face API. The results have shown the FRTC is more accurate, 7.02% and ~1% than OpenCV and Face API respectively. © 2021 IEEE.

关键词： pattern matching

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