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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Learning a Facial Expression Embedding Disentangled from Identity

Learning a Facial Expression Embedding Disentangled from Ide...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Wei Ji, Xianpeng Chen, Keyu Ding, Yu Fan, Changjie Netease Fuxi AI Lab Virtual Human Grp Beijing Peoples R China Univ Sci & Technol China Langfang Peoples R China

ISBN: (纸本)9781665445092

The facial expression analysis requires a compact and identity-ignored expression representation. In this paper, we model the expression as the deviation from the identity by a subtraction operation, extracting a continuous and identity-invariant expression embedding. We propose a Deviation Learning Network (DLN) with a pseudo-siamese structure to extract the deviation feature vector. To reduce the optimization difficulty caused by additional fully connection layers, DLN directly provides high-order polynomial to nonlinearly project the high-dimensional feature to a low-dimensional manifold. Taking label noise into account, we add a crowd layer to DLN for robust embedding extraction. Also, to achieve a more compact representation, we use hierarchical annotation for data augmentation. We evaluate our facial expression embedding on the FEC validation set. The quantitative results prove that we achieve the state-of-the-art, both in terms of fine-grained and identity-invariant property. We further conduct extensive experiments to show that our expression embedding is of high quality for expression recognition, image retrieval, and face manipulation.

关键词： Manifolds computer vision Image recognition Head Annotations Face recognition Image retrieval

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Gesture recognition for Sign Language using Machine Learning 5

Gesture Recognition for Sign Language using Machine Learning

引用

5th ieee Global conference for Advancement in Technology, GCAT 2024

作者： Gupta, Pradeep Kumar Varadharajan, Nayantara Ajith, Keerthana Nair, Priyanka C. Sampath, Nalini Ravindran, Kaushik Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Wonder Smile Pllc TX United States

ISBN: (纸本)9798350376685

Millions of deaf and hard-of-hearing people globally rely on sign language as the fundamental tool for communication. However, a huge communication gap exists between the sign language users and the larger community which greatly hinders their interaction in all aspects. In order to improve communication for individuals who rely on sign language, this research aims to address this issue by creating an advanced gesture recognition model that can properly identify and understand sign language motions from video. This system interprets sign language motions from streaming video streams using computer vision and machine learning. It facilitates translation of these signs into text or voice making sure that there are no impediments to communication and everybody feels included. Consequently, this technology opens up possibilities of a more connected society, towards inclusive societies that are friendlier to both users of sign languages and other members of society. In conclusion, the project is about inventing an innovative approach that would help empower sign language users by removing limitations associated with effective communication within our world today. The use of modern technology will enable us to bridge all communication gaps that pose significant challenges, thus promoting greater inclusivity across various dimensions of life since it only knows one way which is forward. © 2024 ieee.

关键词： Gesture recognition

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Machine Learning Approach for Sign Language recognition System Development 3

Machine Learning Approach for Sign Language Recognition Syst...

引用

3rd ieee International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Verma, Rajan Chohan, Jasgurpreet Singh Joshi, Sanjeev Kumar Kaur, Jaspreet Bhatia, Harpreet S. Abbas, Haider Mohammed Chitkara University Chitkara Center For Research And Development Himachal Pradesh India School Of Mechanical Engineering Rayat Bahra University Mohali India Uttaranchal Institute Of Technology Uttaranchal University Department Of Mechanical Engineering Dehradun India Chandigarh Engineering College Chandigarh Group Of Colleges Department Of Computer Science Engineering Punjab Mohali India Chitkara University Centre Of Research Impact And Outreach Punjab Rajpura India The Islamic University Najaf Iraq

ISBN: (纸本)9798350318609

The gesture classification and recognition field offer ample research opportunities due to the growing deaf and hearing-impaired populations and the advancements in vision-based devices. As recognition of hand gestures is crucial to interpreting sign language, gesture recognition systems should consider the maximum number of signs. An ensemble neural network-based robust hand gesture recognition model is presented in this work. Real-time sign detection will be provided by a suggested system in this system. It will facilitate communication with and interaction with challenged people. An American Sign Language (ASL) recognition model can be trained with a convolution neural network (CNN). The proposed model is also suitable for supervised object segmentation. The proposed methodology also performs very well when tested on publicly available datasets of complex hand gestures, with an accuracy rate of 99.111%. © 2024 ieee.

关键词： Support vector machines

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Yoga Pose Detection with Deep Learning and computer vision 15

Yoga Pose Detection with Deep Learning and Computer Vision

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Srivastava, Amisha Gayathri, K. Thangavel, Senthil Kumar Meghana, Pullela Vishvajit, S. Senthil Kumar, B. Somasundaram, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita Vishwa Vidyapeetham Amrita Darshanam International Centre for Spiritual Studies Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India

ISBN: (纸本)9798350370249

An approach to do real-time monitoring of Yoga Asanas using Deep Learning and computer vision approaches. Convolutional neural networks (CNN) and long short-term memory (LSTM) are combined to create a hybrid deep learning model. Human pose recognition can be used to create a selfinstruction exercise system that enables people to learn and perform exercises appropriately on their own as these resources are not always readily available. This project discusses several machine learning and deep learning algorithms to precisely identify yoga positions on pre-recorded films as well as in realtime, laying the groundwork for developing such a system. These kinds of applications are useful during times of lockdown, such as the lockdown we experienced in 2020 due to the coronavirus epidemic, when people's freedom of movement is severely constrained and they may use such programmers' quite easily from home. © 2024 ieee.

关键词： Activity recognition CNN Deep Learning Open Pose Posture Analysis Yoga Asanas

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Deep Learning-Based Women Hairstyle Classification Through Inception Resnet-101

Deep Learning-Based Women Hairstyle Classification Through I...

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2023 International conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Farook, Faazelah Mohamed Mohamed Mansoor Roomi, S. Sasithra Devi, A. Jayanthi Rajee, R.B. Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil-Nadu Madurai India

ISBN: (纸本)9798350393378

Artificial intelligence and computer science's computer vision field is revolutionizing a number of industries, including healthcare, automotive, agriculture, security, and entertainment, by enabling robots to assess visual input. For tasks like object detection, classification, and 3D reconstruction, it makes use of methods from the fields of image processing, machine learning, deep learning, and pattern recognition. One promising application of this technology is the classification of hairstyles using pre-trained networks, which can be used for the detection of criminals who may be using certain hairstyles as a form of disguise or it can be used to identify a person through CCTV surveillance for further investigations. In this paper, a study using the performance of Inception Resnet 101, a popular learning architecture has been used for the classification of several common hairstyles. These hairstyles include 'Bald', 'Braids', 'Free Hair', 'Pony Tail' and 'Short Hair'. Validation accuracy of about 89.7% and testing accuracy of 95.6% has been achieved. This approach demonstrates that the model can attain high accuracy even in the face of difficult real-world circumstances, such as changes in illumination, angles, and distances. A few of the industries that need to take this into account are marketing, trend analysis, and customized haircuts. Approaches utilizing computer vision and artificial intelligence facilitate innovation, enhance decision-making, and enhance the user experience. © 2023 ieee.

关键词： computer vision

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A Universal Encoder Rate Distortion Optimization Framework for Learned Compression

A Universal Encoder Rate Distortion Optimization Framework f...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Jing Li, Bin Li, Jiahao Xiong, Ruiqin Lu, Yan Peking Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665448994

Learning-based image compression has drawn increasing attention in recent years. Despite impressive progress has been made, it still lacks a universal encoder optimization method to seek efficient representation for different images. In this paper, we develop a universal rate distortion optimization framework for learning-based compression, which adaptively optimizes latents and side information together for each image. The proposed framework is independent of network architecture and can be flexibly applied to existing and potential future compression networks. Experimental results demonstrate that we can achieve 6.6% bit rate saving against the latest traditional codec, i.e., VVC, yielding the state-of-the-art compression ratio. Moreover, with the proposed optimization framework, we win the first place in CLIC validation phase for all the three different bit rates in terms of PSNR.

关键词： computer vision Image coding Codecs conferences Bit rate Rate-distortion Optimization methods

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A Strong Baseline for Vehicle Re-Identification

A Strong Baseline for Vehicle Re-Identification

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Huynh, Su, V Nguyen, Nam H. Nguyen, Ngoc T. Nguyen, Vinh T. Q. Huynh, Chau Chuong Nguyen Cybercore AI Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. The code is available at https://***/cybercore-co-ltd/track2_aicity_2021.

关键词： Training computer vision Target tracking Head conferences Urban areas Stacking

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Real Time pattern recognition with Support Vector Machines and Local Binary patterns 1

引用

1st International conference on Advanced Computing Techniques in Engineering and Technology, ACTET 2023

作者： Baby, Ann Akhil Kumar, K.M. Kerala Kalamassery India

ISBN: (数字)9783031541629

ISBN: (纸本)9783031541612

This research introduces a facial recognition-based attendance system that leverages an open-source computer vision library and integrates with a real-time database system. The system comprises essential components, including a camera for image capture, a computer for image processing, and a database containing registered facial data. As individuals enter a room, the camera captures their images, which are then subjected to facial detection and recognition by the computer. The system conducts a comparison between the detected faces and the pre-registered facial data in the database, subsequently logging the attendance of individuals who are successfully recognized. Moreover, the system offers functionality for the inclusion of new facial data into the database, facilitating future attendance tracking. To gauge the system's performance, an assessment was carried out using a dataset of 100 images. The results indicate an accuracy rate of 95% in both facial detection and recognition tasks. This system holds promise for a wide range of practical applications in diverse environments, such as educational institutions, workplaces, and event management, by automating attendance management processes and alleviating the burdens associated with manual record-keeping. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cameras

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Sentiment Analysis in Turkish Sign Language Through Facial Expressions and Hand Gestures 32

Sentiment Analysis in Turkish Sign Language Through Facial E...

引用

32nd ieee Signal Processing and Communications Applications conference (SIU)

作者： Takir, Seyma Bilen, Baris Arslan, Dogukan Istanbul Tech Univ Yapay Zeka & Veri Muhendisligi Istanbul Turkiye

ISBN: (纸本)9798350388978;9798350388961

In sign languages, where communication is achieved through hand gestures, facial expressions and body language, signs are the subject of many studies due to the diversity in terms of the position of different body parts. These diversities are also encountered in emotion detection in Turkish Sign Language (TID), making direct translation of hand gestures inadequate for emotion detection. Accordingly, in this study, for the first time in the literature, sentiment analysis in TID was performed using facial expressions and hand gestures. For this purpose, a specialized model for the tasks of emotion extraction from facial expressions and gesture recognition from hand gestures was fine-tuned with the dataset collected in this study. As a result, facial expressions are found to be more significant than hand gestures in sentiment analysis in TID, but when supported with hand gestures, the performance improved even more.

关键词： Turkish Sign Language sentiment analysis computer vision

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Scaled 360 layouts: Revisiting non-central panoramas

Scaled 360 layouts: Revisiting non-central panoramas

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Berenguel-Baeta, Bruno Bermudez-Cameo, Jesus Guerrero, Jose J. Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9781665448994

From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In this work we present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We obtain the boundaries of the structural lines of the room from a non-central panorama using deep learning and exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout. We solve the problem for Manhattan environments, handling occlusions, and also for Atlanta environments in an unified method. The experiments performed improve the state-of-the-art methods for 3D layout recovery from a single panorama. Our approach is the first work using deep learning with non-central panoramas and recovering the scale of single panorama layouts.

关键词： Deep learning computer vision Three-dimensional displays Sensitivity conferences Layout Neural networks

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