2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)最新文献_第5页

Toward a Scoping Review of Social Intelligence in Virtual Humans 虚拟人的社会智能研究综述

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2023-01-05 DOI: 10.1109/FG57933.2023.10042532

S. Mozgai, Sarah Beland, Andrew Leeds, Jade G. Winn, Cari Kaurloto, D. Heylen, Arno Hartholt

引用次数: 0

Examining Subject-Dependent and Subject-Independent Human Affect Inference from Limited Video Data 从有限的视频数据检验主体依赖和主体独立的人类情感推断

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2023-01-05 DOI: 10.1109/FG57933.2023.10042798

R. Parameshwara, Ibrahim Radwan, Subramanian Ramanathan, Roland Goecke

{"title":"Examining Subject-Dependent and Subject-Independent Human Affect Inference from Limited Video Data","authors":"R. Parameshwara, Ibrahim Radwan, Subramanian Ramanathan, Roland Goecke","doi":"10.1109/FG57933.2023.10042798","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042798","url":null,"abstract":"Continuous human affect estimation from video data entails modelling the dynamic emotional state from a sequence of facial images. Though multiple affective video databases exist, they are limited in terms of data and dy-namic annotations, as assigning continuous affective labels to video data is subjective, onerous and tedious. While studies have established the existence of signature facial expressions corresponding to the basic categorical emotions, individual differences in emoting facial expressions nevertheless exist; factoring out these idiosyncrasies is critical for effective emotion inference. This work explores continuous human affect recognition using AFEW-VA, an ‘in-the-wild’ video dataset with limited data, employing subject-independent (SI) and subject-dependent (SD) settings. The SI setting involves the use of training and test sets with mutually exclusive subjects, while training and test samples corresponding to the same subject can occur in the SD setting. A novel, dynamically-weighted loss function is employed with a Convolutional Neural Network (CNN)-Long Short- Term Memory (LSTM) architecture to optimise dynamic affect prediction. Superior prediction is achieved in the SD setting, as compared to the SI counterpart.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132674468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Video Inference for Human Mesh Recovery with Vision Transformer 基于视觉变换的人体网格恢复视频推理

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2023-01-05 DOI: 10.1109/FG57933.2023.10042731

Hanbyel Cho, Jaesung Ahn, Yooshin Cho, Junmo Kim

引用次数: 2

DisVAE: Disentangled Variational Autoencoder for High-Quality Facial Expression Features DisVAE:高质量面部表情特征的解纠缠变分自编码器

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2023-01-05 DOI: 10.1109/FG57933.2023.10042668

Tianhao Wang, Mingyue Zhang, Lin Shang

引用次数: 0

Improving Performance of Facial Biometrics With Quality-Driven Dataset Filtering 用质量驱动的数据集滤波提高面部生物识别性能

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2023-01-05 DOI: 10.1109/FG57933.2023.10042579

Iurii Medvedev, Nuno Gonçalves

引用次数: 0

An efficient approach for real-time abnormal human behavior recognition on surveillance cameras 一种有效的监控摄像机实时异常行为识别方法

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2023-01-05 DOI: 10.1109/FG57933.2023.10042648

Ngoc Hoang Nguyen, Nhat Nguyen Xuan, T. Bui, Dao Huu Hung, S. Q. Truong, V. Hoang

{"title":"An efficient approach for real-time abnormal human behavior recognition on surveillance cameras","authors":"Ngoc Hoang Nguyen, Nhat Nguyen Xuan, T. Bui, Dao Huu Hung, S. Q. Truong, V. Hoang","doi":"10.1109/FG57933.2023.10042648","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042648","url":null,"abstract":"In recent years, abnormal human behavior recognition has become an attractive research topic of computer vision due to the rapid growth of demand to monitor human activities on closed-circuit television (CCTV) cameras. However, developing a deep learning-based model for abnormal/violent behavior recognition in surveillance systems is still quite challenging and costly due to inadequate data and model complexity. This paper presents an efficient approach to recognize violent behavior such as fighting, sexual harassment, and climbing fence in real-time on a multi-camera-one-edge-device system. Our approach develops a lightweight 3DCNN model trained by an effective optimization process to recognize human behavior from sequence frames of CCTV video signal input. In the optimization method, we utilize two advantages of deep learning techniques of knowledge distillation and contrastive learning to enhance the quality of the lightweight model on recognizing recorded human behaviors, which can help the student network learn distilled information from both the bigger model and contrastive object representations. We also establish a large CCTV human behavior video dataset containing 4,200 abnormal and 24,000 normal videos. The effectiveness of the proposed approach is shown by the high inference performance and impressive results evaluated on both public datasets the RWF-2000 dataset, the UCF101 dataset, and our collected datasets.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"323 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115909612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the Efficacy of a Self-Stigma Reduction Mental Health Program with Mobile Biometrics: Work-in-Progress 用移动生物识别技术评估自我耻辱减少心理健康项目的效果:正在进行的工作

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2023-01-05 DOI: 10.1109/FG57933.2023.10042655

Nele Loecher, S. King, J. Cabo, T. Neal, Kristin A Kosyluk

{"title":"Assessing the Efficacy of a Self-Stigma Reduction Mental Health Program with Mobile Biometrics: Work-in-Progress","authors":"Nele Loecher, S. King, J. Cabo, T. Neal, Kristin A Kosyluk","doi":"10.1109/FG57933.2023.10042655","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042655","url":null,"abstract":"One of the strongest predictors of success in post-secondary education is student engagement. Unfortunately, people with psychiatric disabilities are less engaged in their campus communities. This work-in-progress paper details the disclosure-based self-stigma reduction program, Up To Me, which is developed to increase inclusion and engagement of people with mental illness on college campuses by teaching strategies to weigh costs and benefits of disclosing one's mental illness. Further, we elaborate on the program's evaluation mechanisms, which involve both self-reported and passively recorded smartphone sensor data. The latter reflects a unique merging of behavioral and computer sciences that serves to facilitate behavioral modeling using artificial intelligence as an objective measure of Up to Me outcomes. Similar to data collection for some activity and biometric recognition applications, we employ a publicly available and free-to-use smartphone sensor reading app to correlate self-reported well-being with Up to Me participant behaviors. We anticipate that the behavioral data gathered via smartphones will substantiate self-report data on Up to Me outcomes.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125637334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic face imaging: a novel analysis framework for 4D social face perception and expression 动态面部成像:4D社会面部感知和表达的新分析框架

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2023-01-05 DOI: 10.1109/FG57933.2023.10042724

L. Snoek, Rachael E. Jack, P. Schyns

{"title":"Dynamic face imaging: a novel analysis framework for 4D social face perception and expression","authors":"L. Snoek, Rachael E. Jack, P. Schyns","doi":"10.1109/FG57933.2023.10042724","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042724","url":null,"abstract":"Measuring facial expressions is a notoriously difficult and time-consuming process, often involving manual labeling of low-dimensional descriptors such as Action Units (AUs). Computer vision algorithms provide automated alternatives for measuring and annotating face shape and expression from 2D images, but often ignore the complexities of dynamic 3D facial expressions. Moreover, open-source implementations are often difficult to use, preventing widespread adoption by the wider scientific community beyond computer vision. To address these issues, we develop dynamic face imaging, a novel analysis framework to study social face perception and expression. We use state-of-the-art 3D face reconstruction models to quantify face movement as temporal shape deviations in a common 3D mesh topology, which disentangles global (head) movement and local (facial) movement. Using a set of validation analyses, we test different reconstruction algorithms and quantify how well they reconstruct facial “action units” and track key facial landmarks in 3D, demonstrating promising performance and highlight areas for improvement. We provide an open-source software package that implements functionality for easy reconstruction, preprocessing, and analysis of these dynamic facial expression data.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127833639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

SignNet: Single Channel Sign Generation using Metric Embedded Learning SignNet:使用度量嵌入式学习生成单通道符号

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-12-06 DOI: 10.1109/FG57933.2023.10042711

Tejaswini Ananthanarayana, Lipisha Chaudhary, Ifeoma Nwogu

{"title":"SignNet: Single Channel Sign Generation using Metric Embedded Learning","authors":"Tejaswini Ananthanarayana, Lipisha Chaudhary, Ifeoma Nwogu","doi":"10.1109/FG57933.2023.10042711","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042711","url":null,"abstract":"A true interpreting agent not only understands sign language and translates to text, but also understands text and translates to signs. Much of the AI work in sign language translation to date has focused mainly on translating from signs to text. Towards the latter goal, we propose a text-to-sign translation model, SignNet, which exploits the notion of similarity (and dissimilarity) of visual signs in translating. This module presented is only one part of a dual-learning two task process involving text-to-sign (T2S) as well as sign-to-text (S2T). We currently implement SignNet as a single channel architecture so that the output of the T2S task can be fed into S2T in a continuous dual learning framework. By single channel, we refer to a single modality, the body pose joints. In this work, we present SignNet, a T2S task using a novel metric embedding learning process, to preserve the distances between sign embeddings relative to their dissimilarity. We also describe how to choose positive and negative examples of signs for similarity testing. From our analysis, we observe that metric embedding learning-based model perform significantly better than the other models with traditional losses, when evaluated using BLEU scores. In the task of gloss to pose, SignNet performed as well as its state-of-the-art (SoTA) counterparts and outperformed them in the task of text to pose, by showing noteworthy enhancements in BLEU 1 - BLEU 4 scores (BLEU 1: 31 → 39; ≈26% improvement and BLEU 4: 10.43 →11.84; ≈14% improvement) when tested on the popular RWTH PHOENIX-Weather-2014T benchmark dataset","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"21 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116943448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss 基于多任务学习和单侧元三重损失的广义人脸防欺骗

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-11-29 DOI: 10.1109/FG57933.2023.10042685

Chu-Chun Chuang, Chien-Yi Wang, S. Lai

引用次数: 1