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Class preserving projections and data augmentation for appearance-based face recognition

PATTERN ANALYSIS AND applications 2025年第1期28卷 1-12页

作者： Soldera, John Scharcanski, Jacob Inst Fed Farroupilha Estr RS-218 BR-98806700 St Angelo Rio Grande do S Brazil Univ Fed Rio Grande do Sul Inst Informat Ave Bento Goncalves 9500 BR-91501970 Porto Alegre Rio Grande do S Brazil

Computer vision and Biometrics benefit from the recent advances in Pattern Recognition and Artificial Intelligence, which tends to make model-based face recognition more efficient. Also, deep learning combined with data augmentation tends to enrich the training sets used for learning tasks. Nevertheless, face recognition still is challenging, especially because of imaging issues that occur in practice, such as changes in lighting, appearance, head posture and facial expression. In order to increase the reliability of face recognition, we propose a novel supervised appearance-based face recognition method which creates a low-dimensional orthogonal subspace that enforces the face class separability. The proposed approach uses data augmentation to mitigate the problem of training sample scarcity. Unlike most face recognition approaches, the proposed approach is capable of handling efficiently grayscale and color face images, as well as low and high-resolution face images. Moreover, proposed supervised method presents better class structure preservation than typical unsupervised approaches, and also provides better data preservation than typical supervised approaches as it obtains an orthogonal discriminating subspace that is not affected by the singularity problem that is common in such cases. Furthermore, a soft margins Support Vector machine classifier is learnt in the low-dimensional subspace and tends to be robust to noise and outliers commonly found in practical face recognition. To validate the proposed method, an extensive set of face identification experiments was conducted on three challenging public face databases, comparing the proposed method with methods representative of the state-of-the-art. The proposed method tends to present higher recognition rates in all databases. In addition, the experiments suggest that data augmentation also plays an essential role in the appearance-based face recognition, and that the CIELAB color space (L*a*b) is generally mor

关键词： Pattern recognition image processing Appearance-based face recognition Supervised methods Dimensionality reduction

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A Survey on Graph Neural Networks and its applications in Various Domains

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SN Computer Science 2025年第1期6卷 1-12页

作者： Murgod, Tejaswini R. Reddy, P. Srihith Gaddam, Shamitha Sundaram, S. Meenakshi Anitha, C. Department of Artificial Intelligence & Machine Learning BNM Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Karnataka Bengaluru India

Graph Neural Networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph Neural Networks (GNNs), such as graph recurrent networks (GRN), graph attention networks (GAT), and graph convolutional networks (GCN), have shown remarkable results on a variety of deep learning tasks in recent years. In this study, we offer a generic design pipeline for GNN models, go over the variations of each part, classify the applications in an organized manner, and suggest four outstanding research issues. Dealing with graph data, which provides extensive connection information among pieces, is necessary for many learning tasks. A model that learns from graph inputs is required for modelling physics systems, learning molecular fingerprints, predicting protein interfaces, and identifying illnesses. Reasoning on extracted structures (such as the dependency trees of sentences and the scene graphs of photos) is an important research issue that also requires graph reasoning models in other domains, such as learning from non-structural data like texts and images. Graph Neural Networks (GNNs) are primarily designed for dealing with graph-structured data, where relationships between entities are modeled as edges in a graph. While GNNs are not traditionally applied to image classification problems, researchers have explored ways to leverage graph-based structures to enhance the performance of Convolutional Neural Networks (CNNs) in certain scenario. GNN have been increasingly applied to Natural Language processing (NLP) tasks, leveraging their ability to model structured data and capture relationships between elements in a graph. GNN are also applied for traffic related problems particularly in modeling and optimizing traffic flow, analyzing transportation networks, and addressing congestion issues. GNN can be used for traffic flow prediction, dynamic routing & navigation, Anomaly detection, public transport network

关键词： Computer vision Graph neural networks Intrusion detection Natural language processing Neural networks Traffic control

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High Dynamic Range image processing Techniques for Automation and machine vision

High Dynamic Range Image Processing Techniques for Automatio...

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Automation, Robotics and Computer Engineering (ICARCE), International Conference on

作者： Chunhao Li Zhejiang University of Science and Technology Hangzhou China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

High Dynamic Range (HDR) imaging has become a significant technological advancement in visual data processing, allowing for the capture of a wider dynamic range of luminance levels in images. This paper explores various HDR processing techniques and their potential applications in automation and machine vision. By using methods such as multiple image fusion, image registration, and tone mapping, the paper demonstrates how HDR processing can enhance visual data in automated systems, improving accuracy in environments requiring complex lighting conditions. This work applies HDR algorithms to real-world scenarios, showcasing their potential in industrial automation and robotics, where accurate visual data plays a crucial role.

关键词： Visualization image registration Automation Accuracy Service robots machine vision Lighting Real-time systems High dynamic range image fusion

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Unleashing Sustainable Efficiency: The Integration of Computer vision into Industry 4.0

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ENGINEERING MANAGEMENT JOURNAL 2024年

作者： Gill, Rupali Srivastava, Durgesh Hooda, Susheela Singla, Chaitanya Chaudhary, Rekha Chitkara Univ Chandigarh Punjab India Lovely Profess Univ Bengaluru India

The Internet of Things, artificial intelligence, machine learning, and big data are just a few of the cutting-edge technologies that are being integrated into manufacturing processes as part of the "Industry 4.0" revolution. Computer vision is an essential component of Industry 4.0 regarding sustainability, developed as a disruptive technology that extracts and interprets visual information from digital photos or videos using image processing techniques and advanced models. In the context of Industry 4.0, this article offers an overview of computer vision, including its associated prospects, difficulties, and applications. A particular emphasis is placed on sustainability. It explores computer vision applications in robotics and automation, safety and security, process optimization, augmented reality, robotics and inspection, object identification and tracking, predictive maintenance, and quality control and inspection. The study also identifies the critical approaches used to overcome the difficulties in implementing computer vision solutions. Incorporating computer vision into Industry 4.0 holds promise for unleashing unprecedented levels of efficiency, novelty, and competitiveness in the industrial sector. The manufacturing and industrial sectors may use Industry 4.0's prospects and adopt sustainable practices by utilizing computer vision and overcoming its inherent limits. This will help to create an eco-conscious and efficient future.

关键词： Computer vision Industry 4.0 Deep Learning machine Learning Industry Sustainability SDG New Product Development supply chain management sustainability technology management

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Low-Cost Hardware-Accelerated vision-Based Depth Perception for Real-Time applications

Low-Cost Hardware-Accelerated Vision-Based Depth Perception ...

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International Conference on Computer vision and machine Intelligence, CVMI 2022

作者： Aditya, N.G. Dhruval, P.B. Shylaja, S.S. Katharguppe, Srinivas Department of Computer Science PES University Karnataka Bengaluru560085 India

ISBN: (纸本)9789811978661

Depth estimation and 3D object detection are critical for autonomous systems to gain context of their surroundings. In recent times, compute capacity has improved tremendously, enabling computer vision and AI on the edge. In this paper, we harness the power of CUDA and OpenMP to accelerate ELAS (a stereoscopic vision-based disparity calculation algorithm) and 3D projection of the estimated depth while performing object detection and tracking. We also examine the utility of Bayesian inference in achieving real-time object tracking. Finally, we build a drive-by-wire car equipped with a stereo camera setup to test our system in the real world. The entire system has been made public and easily accessible through a Python module. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stereo image processing

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3D Printing Design Simulation of Ceramic Products Based on 3D image Reproduction Technology 4th

3D Printing Design Simulation of Ceramic Products Based on 3...

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4th International Conference on machine Learning, image processing, Network Security and Data Sciences (MIND)

作者： Yao, Lan Shanghai Art & Design Acad Shanghai Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the development of communication technology, people will be exposed to more and more graphics and images in the process of life andwork. Like using digital devices such as camera, scanner and camera to obtain images, but these instruments and equipment can only obtain two-dimensional image information of objects, which is completely insufficient. Inmany fields, three-dimensional information of objects is necessary. In this paper, the 3D printing design of ceramic products is simulated based on 3D image reproduction technology. The satisfaction of users with the ceramic visual effect and hand-held comfort produced by 3D image reproduction simulation technology is investigated by means of questionnaire, and the computer vision technology and stereo matching technology are compared. The results show that more than 85% of users are very satisfied with the ceramic visual effect and hand-held comfort of three-dimensional image reproduction simulation technology, and less than 5% of users are not satisfied;The satisfaction of ceramic visual effect produced by computer vision technology and stereo matching technology is less than 60%, and the hand-held comfort is less than 70%.

关键词： 3D image reproduction technology Ceramic products 3D printing design Simulation research

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Detection and Counting of Nuts and Bolts with image processing Using MATLAB 1

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2nd Conference of Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2020

作者： Murali, Gunji Bala Vasant, Gaikwad Tejas Kupwade, Dhananjay D. Girish, Deshmukh Atharva Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9789811902963

ISBN: (纸本)9789811902956

machine vision system plays vital roles in the industrial application in order to maintain quality and control the process. machine vision technology has numerous applications in various industries like automotive industry, pharmaceutical industries, food and beverage, electronics, packages, and process control. The purpose behind this work is the awareness of machine vision technology and the identification of mechanical components particularly nuts and bolts. An automotive uses nuts and bolts and are assembled at different stages of assembly. The count of these nuts and bolts becomes vital in order to avoid shortage at any stage of assembly. Thus, sorting of the nuts and bolts and their counting is necessary in order to save time. The system can be designed using different algorithms, and all algorithms can be integrated using MATLAB software. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image processing technique machine vision MATLAB Nuts bolts counting

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Hand Gesture-Based AI System for Accessing Windows applications 4th

Hand Gesture-Based AI System for Accessing Windows Applicati...

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4th International Conference on image processing and Capsule Networks, ICIPCN 2023

作者： Palivela, Lakshmi Harika Premanand, V. Begum, Afruza School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819970926

Actions speak more than words. In the context of the above statement, the importance of gestures and using them to control a system has become popular. The hand gesture recognition system for opening applications in Windows is a computer vision application that uses a webcam and machine learning models to recognize hand gestures and open specific applications. This system allows users to control their computers using simple hand gestures instead of relying on traditional input devices such as keyboards or mouse. The system uses the Tensor Flow and Media-Pipe libraries for training and detecting hand gestures, respectively. The trained machine learning model recognizes the gestures, and specific applications are opened based on the recognized gesture. The system has potential applications in accessibility, gaming, and virtual reality. In this work, we will discuss potential applications of the hand gesture recognition system for opening applications in Windows, and the second proposed system, which may also act as the control system of PowerPoint presentations, can be made easy and convenient using these gestures-based control. Thus, the proposed model improves the interaction between the human and the computer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Palmprint recognition

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Impact of image size on Human Facial Expression Recognition: A Relative Study 10

Impact of image size on Human Facial Expression Recognition:...

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10th International Conference on Communication and Signal processing, ICCSP 2024

作者： Swathi, A. Menon, Shashank Bhavana, V. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communications Engineering Bengaluru India

ISBN: (纸本)9798350353068

The recognition of facial emotions has received growing focus in recent years due to its importance and the significant role it plays in shaping the way humans interact with computers. This can be achieved using deep learning algorithms which are currently being used for various vision-based applications like self-driving vehicles. In this paper, we used the Convolutional Neural Network (CNN) for training and testing images with different facial expressions using the TensorFlow machine learning library. The application is comprised of two stages: a recognizer for validation purposes and a data training model that is used to train the data. The Data training model uses CNN to train data. With the help of this algorithm, the system can recognize the six universal emotions, anger, disgust, joy, surprise, sadness, and fear, along with neutral. The experiment demonstrated that the outcomes of the training program were significantly influenced by the image size that is used for training. As a result, both the RGB and grayscale images yielded comparable results, whereas images with different sizes exhibited variations in performance. The RGB images achieved a training accuracy of 99.666%, a validation accuracy of 99.95%, and a test accuracy of 32.28%, while the combination of RGB and grayscale images attained a training accuracy of 98.99%, a validation accuracy of 92.76%, and a test accuracy of 72.56%. © 2024 IEEE.

关键词： Convolutional neural networks

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Attention to Emotions: Body Emotion Recognition In-the-Wild Using Self-attention Transformer Network 18th

Attention to Emotions: Body Emotion Recognition In-the-Wild ...

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18th International Joint Conference on Computer vision, Imaging and Computer Graphics Theory and applications (VISIGRAPP) / 18th International Conference on Computer Graphics Theory and applications

作者： Paiva, Pedro V. V. Ramos, Josue J. G. Gavrilova, Marina Carvalho, Marco A. G. Univ Estadual Campinas Sch Technol Limeira Brazil Renato Archer IT Ctr Cyber Phys Syst Div Campinas Brazil Univ Calgary Dept Comp Sci Calgary AB Canada

ISBN: (纸本)9783031667428;9783031667435

Body movements are an essential part of non-verbal communication as they help to express and interpret human emotions. The potential of Body Emotion Recognition (BER) is immense, as it can provide insights into user preferences, automate real-time exchanges and enable machines to respond to human emotions. BER finds applications in customer service, healthcare, entertainment, emotion-aware robots, and other areas. While face expression-based techniques are extensively researched, detecting emotions from body movements in the realworld presents several challenges, including variations in body posture, occlusions, and background. Recent research has established the efficacy of transformer deep-learning models beyond the language domain to solve video and image-related problems. A key component of transformers is the self-attention mechanism, which captures relationships among features across different spatial locations, allowing contextual information extraction. In this study, we aim to understand the role of body movements in emotion expression and to explore the use of transformer networks for body emotion recognition. Our method proposes a novel linear projection function of the visual transformer, which enables the transformation of 2D joint coordinates into a conventional matrix representation. Using an original method of contextual information learning, the developed approach enables a more accurate recognition of emotions by establishing unique correlations between individual's body motions over time. Our results demonstrated that the self-attention mechanism was able to achieve high accuracy in predicting emotions from body movements, surpassing the performance of other recent deep-learning methods. In addition, the impact of dataset size and frame rate on classification performance is analyzed.

关键词： Body emotion recognition Affective computing Video and image processing Gait analysis Attention-based design

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