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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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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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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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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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Facial Emotion recognition Using CNNs: Implications for Affective computing and Surveillance

Facial Emotion Recognition Using CNNs: Implications for Affe...

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international conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Vishnu Kant Panchal Sandeep Govindrao Shubham Mahajan Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dept of Computer Engineering G H Raisoni College of Engineering and Management Pune Department Of Computer Science Christ Deemed to Be University Delhi-NCR

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Human-computer interaction depends on facial expression detection, which also finds utility in mental health monitoring to improve digital environment user experiences. Perfect spotting Computer vision has always baffled emotions including rage, disdain, disgust, fear, happiness, neutrality, grief, and surprise. Since they can automatically generate hierarchical feature representations from raw visual data, CNN have been rather helpful tools in tackling these problems. This article presents a CNN-based method for facial emotion prediction by using the power of the network to detect minute changes in face features linked with different emotional states. Having been trained on a large collection of annotated facial photos, the suggested model achieved final accuracy of 92%. The results reveal in emotional computing, surveillance, and automated customer service the effectiveness and most likely applications of the model. This study helps sustainable digital technologies—, which might be applied in health, education, and well-being—to be always developed.

关键词： Emotion recognition Visualization Accuracy Computational modeling Face recognition Surveillance soft sensors Education Mental health User experience

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Meta Transductive Linear Probing for Few-Shot Node Classification

Meta Transductive Linear Probing for Few-Shot Node Classific...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Mingjing Lan Ningbo Huang Gang Zhou Yi Xia State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Graph few-shot learning aims to predict well by training with very few labeled data. Meta learning has been the most popular solution for few-shot learning problem. However, transductive linear probing shows that fine-tuning a simple linear classification head after a pretrained graph neural networks can outperforms most of the sophisticated-designed graph meta learning algorithms. Therefore, in the paper, we propose a meta transductive linear probing methods named Meta-TLP to incorporate the advantages of graph self-supervised and graph meta learning model. Specifically, the graph neural network is firstly pretrained with graph contrastive learning methods. Then we design an unsupervised meta training task construction methods to require meta tasks without relying on labeled data. Finally, we meta training the linear classification head on the meta training tasks to learn to fast adopt to novel classes. Experiment results show that our model can perform better than TLP on three real world datasets.

关键词： Training Metalearning Design methodology Data visualization Prediction algorithms Magnetic heads Data models Graph neural networks pattern recognition Few shot learning

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric pattern recognition Systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

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2023 IEEE international Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Kulwa, Frank Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Oyemakinde, Tolulope Tofunmi Obe, Olumide Olayinka Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China University of Derby School of Computing and Engineering DerbyDE22 3AW United Kingdom Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9781665493840

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario ( © 2023 IEEE.

关键词： Electrodes

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6th international conference on Big Data Cloud and Internet of Things, BDIoT 2022

6th International Conference on Big Data Cloud and Internet ...

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6th international conference on Big Data Cloud and Internet of Things, BDIoT 2022

ISBN: (纸本)9783031283864

The proceedings contain 49 papers. The special focus in this conference is on Big Data Cloud and Internet of Things. The topics include: Knowledge Embeddings for Explainable Recommendation;Design of Blended Learning Course Based on SPOC for Primary School Teachers: Case of soft Skills;the Impact of the Online Flipped Classroom on the Learning Outcomes and Motivation of Nursing Students in Computer Science;digitizing Writing: An Empirical Exploratory Study Towards Students’ Writing Skill Fluency Improvement in ‘Padletosphere’;AraBERT with GANs for High Performance Fine-Grained Dialect Classification;genetic-Novelty Oversampling Technique for Imbalanced Data;neural Network for Arabic Text Diacritization on a New Dataset;processing Time Performance Analysis of Scheduling Algorithms for Virtual Machines Placement in Cloud computing Environment;Business - IT Alignment in Cloud Environment Comparative Study;machine Learning-Based Intrusion Detection System: Review and Taxonomy;reduce Cheating in e-Exams Using Machine Learning: State of the Art;comparative Study of Bayesian Optimization Process for the Best Machine Learning Hyperparameters;The Effect of Normalization and Batch Normalization Layers in CNNs Models: Application to Plant Disease Classifications;the Role of Ground Truth Annotation in Semantic Image Segmentation Performance for Autonomous Driving;fine-Tuning Pre-trained Vision Transformer Model for Anomaly Detection in Video Sequences;An Efficient Real-Time Moroccan Automatic License Plate recognition System Based on the YOLO Object Detector;evaluation of Selected Machine Learning Models and Features for Electrical Consumption Prediction in Educational Institutions;Credit Card Fraud Detection Using SVM, Decision Tree and Random Forest Supervised Machine Learning Algorithms;SNDAE: Self-Normalizing Deep AutoEncoder for Recommendation;Intelligent Evaluation System Using NLP;retrospective Study and Evaluation of School Failure (Junior High School) in Scientific Subj

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