2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)最新文献_第6页

CoNFies: Controllable Neural Face Avatars CoNFies:可控制的神经面部化身

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-11-16 DOI: 10.1109/FG57933.2023.10042763

Heng Yu, Koichiro Niinuma, László A. Jeni

引用次数: 1

Unsupervised Face Recognition using Unlabeled Synthetic Data 使用未标记合成数据的无监督人脸识别

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-11-14 DOI: 10.1109/FG57933.2023.10042627

F. Boutros, Marcel Klemt, Meiling Fang, Arjan Kuijper, N. Damer

{"title":"Unsupervised Face Recognition using Unlabeled Synthetic Data","authors":"F. Boutros, Marcel Klemt, Meiling Fang, Arjan Kuijper, N. Damer","doi":"10.1109/FG57933.2023.10042627","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042627","url":null,"abstract":"Over the past years, the main research innovations in face recognition focused on training deep neural networks on large-scale identity-labeled datasets using variations of multi-class classification losses. However, many of these datasets are retreated by their creators due to increased privacy and ethical concerns. Very recently, privacy-friendly synthetic data has been proposed as an alternative to privacy-sensitive authentic data to comply with privacy regulations and to ensure the continuity of face recognition research. In this paper, we propose an unsupervised face recognition model based on unlabeled synthetic data (USynthFace). Our proposed USynthFace learns to maximize the similarity between two augmented images of the same synthetic instance. We enable this by a large set of geometric and color transformations in addition to GAN-based augmentation that contributes to the USynthFace model training. We also conduct numerous empirical studies on different components of our USynthFace. With the proposed set of augmentation operations, we proved the effectiveness of our USynthFace in achieving relatively high recognition accuracies using unlabeled synthetic data. The training code and pretrained model are publicly available under https://github.com/fdbtrs/Unsupervised-Face-Recognition-using-Unlabeled-Synthetic-Data.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124095105","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: 13

Pain Detection in Masked Faces during Procedural Sedation 手术镇静过程中蒙面疼痛检测

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-11-12 DOI: 10.1109/FG57933.2023.10042502

Y. Zarghami, S. Mafeld, A. Conway, B. Taati

{"title":"Pain Detection in Masked Faces during Procedural Sedation","authors":"Y. Zarghami, S. Mafeld, A. Conway, B. Taati","doi":"10.1109/FG57933.2023.10042502","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042502","url":null,"abstract":"Pain monitoring is essential to the quality of care for patients undergoing a medical procedure with sedation. An automated mechanism for detecting pain could improve sedation dose titration. Previous studies on facial pain detection have shown the viability of computer vision methods in detecting pain in unoccluded faces. However, the faces of patients undergoing procedures are often partially occluded by medical devices and face masks. A previous preliminary study on pain detection on artificially occluded faces has shown a feasible approach to detect pain from a narrow band around the eyes. This study has collected video data from masked faces of 14 patients undergoing procedures in an interventional radiology department and has trained a deep learning model using this dataset. The model was able to detect expressions of pain accurately and, after causal temporal smoothing, achieved an average precision (AP) of 0.72 and an area under the receiver operating characteristic curve (AVC) of 0.82. These results outperform baseline models and show viability of computer vision approaches for pain detection of masked faces during procedural sedation. Cross-dataset performance is also examined when a model is trained on a publicly available dataset and tested on the sedation videos. The ways in which pain expressions differ in the two datasets are qualitatively examined.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132940608","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

Are Face Detection Models Biased? 人脸检测模型有偏见吗?

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-11-07 DOI: 10.1109/FG57933.2023.10042564

S. Mittal, K. Thakral, P. Majumdar, Mayank Vatsa, Richa Singh

{"title":"Are Face Detection Models Biased?","authors":"S. Mittal, K. Thakral, P. Majumdar, Mayank Vatsa, Richa Singh","doi":"10.1109/FG57933.2023.10042564","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042564","url":null,"abstract":"The presence of bias in deep models leads to unfair outcomes for certain demographic subgroups. Research in bias focuses primarily on facial recognition and attribute prediction with scarce emphasis on face detection. Existing studies consider face detection as binary classification into ‘face’ and ‘non-face’ classes. In this work, we investigate possible bias in the domain of face detection through facial region localization which is currently unexplored. Since facial region localization is an essential task for all face recognition pipelines, it is imperative to analyze the presence of such bias in popular deep models. Most existing face detection datasets lack suitable annotation for such analysis. Therefore, we web-curate the Fair Face Localization with Attributes (F2LA) dataset and manually annotate more than 10 attributes per face, including facial localization information. Utilizing the extensive annotations from F2LA, an experimental setup is designed to study the performance of four pre-trained face detectors. We observe (i) a high disparity in detection accuracies across gender and skin-tone, and (ii) interplay of confounding factors beyond demography. The F2LA data and associated annotations can be accessed at http://iab-rubric.org/index.php/F2LA.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123692660","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

The Florence 4D Facial Expression Dataset 佛罗伦萨4D面部表情数据集

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-10-30 DOI: 10.1109/FG57933.2023.10042606

F. Principi, S. Berretti, C. Ferrari, N. Otberdout, M. Daoudi, A. Bimbo

{"title":"The Florence 4D Facial Expression Dataset","authors":"F. Principi, S. Berretti, C. Ferrari, N. Otberdout, M. Daoudi, A. Bimbo","doi":"10.1109/FG57933.2023.10042606","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042606","url":null,"abstract":"Human facial expressions change dynamically, so their recognition / analysis should be conducted by accounting for the temporal evolution of face deformations either in 2D or 3D. While abundant 2D video data do exist, this is not the case in 3D, where few 3D dynamic (4D) datasets were released for public use. The negative consequence of this scarcity of data is amplified by current deep learning based-methods for facial expression analysis that require large quantities of variegate samples to be effectively trained. With the aim of smoothing such limitations, in this paper we propose a large dataset, named Florence 4D, composed of dynamic sequences of 3D face models, where a combination of synthetic and real identities exhibit an unprecedented variety of 4D facial expressions, with variations that include the classical neutral-apex transition, but generalize to expression-to-expression. All these characteristics are not exposed by any of the existing 4D datasets and they cannot even be obtained by combining more than one dataset. We strongly believe that making such a data corpora publicly available to the community will allow designing and experimenting new applications that were not possible to investigate till now. To show at some extent the difficulty of our data in terms of different identities and varying expressions, we also report a baseline experimentation on the proposed dataset that can be used as baseline.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123591720","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

Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis 婴儿面部和上半身对称作为斜颈早期症状的自动评估

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-10-26 DOI: 10.1109/FG57933.2023.10042719

Michael Wan, Xiaofei Huang, Bethany Tunik, S. Ostadabbas

引用次数: 2

SS-VAERR: Self-Supervised Apparent Emotional Reaction Recognition from Video 基于视频的自我监督表观情绪反应识别

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-10-20 DOI: 10.1109/FG57933.2023.10042638

Marija Jegorova, Stavros Petridis, M. Pantic

引用次数: 1

ArabSign: A Multi-modality Dataset and Benchmark for Continuous Arabic Sign Language Recognition 阿拉伯手语:连续阿拉伯手语识别的多模态数据集和基准

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-10-08 DOI: 10.1109/FG57933.2023.10042720

H. Luqman

{"title":"ArabSign: A Multi-modality Dataset and Benchmark for Continuous Arabic Sign Language Recognition","authors":"H. Luqman","doi":"10.1109/FG57933.2023.10042720","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042720","url":null,"abstract":"Sign language recognition has attracted the interest of researchers in recent years. While numerous approaches have been proposed for European and Asian sign languages recognition, very limited attempts have been made to develop similar systems for the Arabic sign language (ArSL). This can be attributed partly to the lack of a dataset at the sentence level. In this paper, we aim to make a significant contribution by proposing ArabSign, a continuous ArSL dataset. The proposed dataset consists of 9,335 samples performed by 6 signers. The total time of the recorded sentences is around 10 hours and the average sentence's length is 3.1 signs. ArabSign dataset was recorded using a Kinect V2 camera that provides three types of information (color, depth, and skeleton joint points) recorded simultaneously for each sentence. In addition, we provide the annotation of the dataset according to ArSL and Arabic language structures that can help in studying the linguistic characteristics of ArSL. To benchmark this dataset, we propose an encoder-decoder model for Continuous ArSL recognition. The model has been evaluated on the proposed dataset, and the obtained results show that the encoder-decoder model outperformed the attention mechanism with an average word error rate (WER) of 0.50 compared with 0.62 with the attention mechanism. The data and code are available at https://github.com/Hamzah-Luqman/rabSign","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124864668","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

Multi-Modal Human Authentication Using Silhouettes, Gait and RGB 基于轮廓、步态和RGB的多模态人体身份验证

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-10-08 DOI: 10.1109/FG57933.2023.10042572

Yuxiang Guo, Cheng Peng, Chun Pong Lau, R. Chellappa

引用次数: 7

StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment StyleMask:用于神经人脸再现的StyleGAN2的风格空间解纠结

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-09-27 DOI: 10.1109/FG57933.2023.10042744

Stella Bounareli, Christos Tzelepis, V. Argyriou, I. Patras, Georgios Tzimiropoulos

{"title":"StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment","authors":"Stella Bounareli, Christos Tzelepis, V. Argyriou, I. Patras, Georgios Tzimiropoulos","doi":"10.1109/FG57933.2023.10042744","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042744","url":null,"abstract":"In this paper we address the problem of neural face reenactment, where, given a pair of a source and a target facial image, we need to transfer the target's pose (defined as the head pose and its facial expressions) to the source image, by preserving at the same time the source's identity characteristics (e.g., facial shape, hair style, etc), even in the challenging case where the source and the target faces belong to different identities. In doing so, we address some of the limitations of the state-of-the-art works, namely, a) that they depend on paired training data (i.e., source and target faces have the same identity), b) that they rely on labeled data during inference, and c) that they do not preserve identity in large head pose changes. More specifically, we propose a framework that, using unpaired randomly generated facial images, learns to disentangle the identity characteristics of the face from its pose by incorporating the recently introduced style space S [1] of StyleGAN2 [2], a latent representation space that exhibits remarkable disentanglement properties. By capitalizing on this, we learn to successfully mix a pair of source and target style codes using supervision from a 3D model. The resulting latent code, that is subsequently used for reenactment, consists of latent units corresponding to the facial pose of the target only and of units corresponding to the identity of the source only, leading to notable improvement in the reenactment performance compared to recent state-of-the-art methods. In comparison to state of the art, we quantitatively and qualitatively show that the proposed method produces higher quality results even on extreme pose variations. Finally, we report results on real images by first embedding them on the latent space of the pretrained generator. We make the code and the pretrained models publicly available at: https://github.com/StelaBou/StyleMask.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131759885","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: 9