2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)最新文献_第7页

T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models T2V-DDPM:使用去噪扩散概率模型的热到可见人脸转换

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-09-19 DOI: 10.1109/FG57933.2023.10042661

Nithin Gopalakrishnan Nair, Vishal M. Patel

{"title":"T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models","authors":"Nithin Gopalakrishnan Nair, Vishal M. Patel","doi":"10.1109/FG57933.2023.10042661","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042661","url":null,"abstract":"Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using visible spectrum images. But, acquiring images in the visible spectrum is impractical in scenarios of low-light and nighttime conditions, and often images are captured in an alternate domain such as the thermal infrared domain. Facial verification in thermal images is often performed after retrieving the corresponding visible domain images. This is a well-established problem often known as the Thermal-to-Visible (T2V) image translation. In this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based solution for T2V translation specifically for facial images. During training, the model learns the conditional distribution of visible facial images given their corresponding thermal image through the diffusion process. During inference, the visible domain image is obtained by starting from Gaussian noise and performing denoising repeatedly. The existing inference process for DDPMs is stochastic and time-consuming. Hence, we propose a novel inference strategy for speeding up the inference time of DDPMs, specifically for the problem of T2V image translation. We achieve the state-of-the-art results on multiple datasets. The code and pretrained models are publically available at http://github.com/Nithin-GK/T2V-DDPM","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126040666","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: 7

The Role of Vocal Persona in Natural and Synthesized Speech 人声角色在自然和合成语音中的作用

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-09-06 DOI: 10.1109/FG57933.2023.10042714

Camille Noufi, Lloyd May, J. Berger

引用次数: 1

Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets 基于动态灰度片段的自监督人脸呈现攻击检测

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-08-27 DOI: 10.1109/FG57933.2023.10042547

Usman Muhammad, Mourad Oussalah

{"title":"Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets","authors":"Usman Muhammad, Mourad Oussalah","doi":"10.1109/FG57933.2023.10042547","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042547","url":null,"abstract":"Face presentation attack detection (PAD) plays an important role in defending face recognition systems against presentation attacks. The success of PAD largely relies on supervised learning that requires a huge number of labeled data, which is especially challenging for videos and often requires expert knowledge. To avoid the costly collection of labeled data, this paper presents a novel method for self-supervised video representation learning via motion prediction. To achieve this, we exploit the temporal consistency based on three RGB frames which are acquired at three different times in the video sequence. The obtained frames are then transformed into grayscale images where each image is specified to three different channels such as R(red), G(green), and B(blue) to form a dynamic grayscale snippet (DGS). Motivated by this, the labels are automatically generated to increase the temporal diversity based on DGS by using the different temporal lengths of the videos, which prove to be very helpful for the downstream task. Benefiting from the self-supervised nature of our method, we report the results that outperform existing methods on four public benchmarks, namely, Replay-Attack, MSU-MFSD, CASIA-FASD, and OULU-NPU. Explainability analysis has been carried out through LIME and Grad-CAM techniques to visualize the most important features used in the DGS.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"38 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113963548","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: 5

Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition 基于骨架的一次性动作识别的部件感知原型图网络

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-08-19 DOI: 10.1109/FG57933.2023.10042671

Tailin Chen, Desen Zhou, Jian Wang, Shidong Wang, Qian He, Chuanyan Hu, Errui Ding, Yuanyuan Guan, Xuming He

{"title":"Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition","authors":"Tailin Chen, Desen Zhou, Jian Wang, Shidong Wang, Qian He, Chuanyan Hu, Errui Ding, Yuanyuan Guan, Xuming He","doi":"10.1109/FG57933.2023.10042671","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042671","url":null,"abstract":"In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel classes, particularly for fine-grained actions. Existing meta-learning frameworks typically rely on the body-level representations in spatial dimension, which limits the generalisation to capture subtle visual differences in the fine-grained label space. To overcome the above limitation, we propose a part-aware prototypical representation for one-shot skeleton-based action recognition. Our method captures skeleton motion patterns at two distinctive spatial levels, one for global contexts among all body joints, referred to as body level, and the other attends to local spatial regions of body parts, referred to as the part level. We also devise a class-agnostic attention mechanism to highlight important parts for each action class. Specifically, we develop a part-aware prototypical graph network consisting of three modules: a cascaded embedding module for our dual-level modelling, an attention-based part fusion module to fuse parts and generate part-aware prototypes, and a matching module to perform classification with the part-aware representations. We demonstrate the effectiveness of our method on two public skeleton-based action recognition datasets: NTU RGB+D 120 and NW-UCLA.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125578026","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: 5

Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks 分析形状和上下文对深度网络人脸识别性能的影响

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-08-05 DOI: 10.1109/FG57933.2023.10042805

Sandipan Banerjee, W. Scheirer, K. Bowyer, P. Flynn

引用次数: 1

Towards Intercultural Affect Recognition: Audio-Visual Affect Recognition in the Wild Across Six Cultures 跨文化情感识别:跨六种文化的野外视听情感识别

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-07-31 DOI: 10.1109/FG57933.2023.10042676

Leena Mathur, R. Adolphs, Maja J. Matari'c

{"title":"Towards Intercultural Affect Recognition: Audio-Visual Affect Recognition in the Wild Across Six Cultures","authors":"Leena Mathur, R. Adolphs, Maja J. Matari'c","doi":"10.1109/FG57933.2023.10042676","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042676","url":null,"abstract":"In our multicultural world, affect-aware AI systems that support humans need the ability to perceive affect across variations in emotion expression patterns across cultures. These systems must perform well in cultural contexts without annotated affect datasets available for training models. A standard assumption in affective computing is that affect recognition models trained and used within the same culture (intracultural) will perform better than models trained on one culture and used on different cultures (intercultural). We test this assumption and present the first systematic study of intercultural affect recognition models using videos of real-world dyadic interactions from six cultures. We develop an attention-based feature selection approach under temporal causal discovery to identify behavioral cues that can be leveraged in intercultural affect recognition models. Across all six cultures, our findings demonstrate that intercultural affect recognition models were as effective or more effective than intracultural models. We identify and contribute useful behavioral features for intercultural affect recognition; facial features from the visual modality were more useful than the audio modality in this study's context. Our paper presents a proof-of-concept and motivation for the future development of intercultural affect recognition systems, especially those deployed in low-resource situations without annotated data.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115435473","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

Face-to-Face Contrastive Learning for Social Intelligence Question-Answering 面向社会智力问答的面对面对比学习

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-07-29 DOI: 10.1109/FG57933.2023.10042612

Alex Wilf, Qianli Ma, P. Liang, Amir Zadeh, Louis-Philippe Morency

引用次数: 5

Mobile Keystroke Biometrics Using Transformers 使用变压器的移动击键生物识别

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-07-15 DOI: 10.1109/FG57933.2023.10042710

Giuseppe Stragapede, Paula Delgado-Santos, Rubén Tolosana, R. Vera-Rodríguez, R. Guest, A. Morales

{"title":"Mobile Keystroke Biometrics Using Transformers","authors":"Giuseppe Stragapede, Paula Delgado-Santos, Rubén Tolosana, R. Vera-Rodríguez, R. Guest, A. Morales","doi":"10.1109/FG57933.2023.10042710","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042710","url":null,"abstract":"Among user authentication methods, behavioural biometrics has proven to be effective against identity theft as well as user-friendly and unobtrusive. One of the most popular traits in the literature is keystroke dynamics due to the large deployment of computers and mobile devices in our society. This paper focuses on improving keystroke biometric systems on the free-text scenario. This scenario is characterised as very challenging due to the uncontrolled text conditions, the influence of the user's emotional and physical state, and the in-use application. To overcome these drawbacks, methods based on deep learning such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been proposed in the literature, outperforming traditional machine learning methods. However, these architectures still have aspects that need to be reviewed and improved. To the best of our knowl-edge, this is the first study that proposes keystroke biometric systems based on Transformers. The proposed Transformer architecture has achieved Equal Error Rate (EER) values of 3.84% in the popular Aalto mobile keystroke database using only 5 enrolment sessions, outperforming by a large margin other state-of-the-art approaches in the literature.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126523107","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: 10

Octuplet Loss: Make Face Recognition Robust to Image Resolution 八组损失:使人脸识别对图像分辨率具有鲁棒性

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-07-14 DOI: 10.1109/FG57933.2023.10042669

Martin Knoche, Mohamed Elkadeem, S. Hörmann, G. Rigoll

{"title":"Octuplet Loss: Make Face Recognition Robust to Image Resolution","authors":"Martin Knoche, Mohamed Elkadeem, S. Hörmann, G. Rigoll","doi":"10.1109/FG57933.2023.10042669","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042669","url":null,"abstract":"Image resolution, or in general, image quality, plays an essential role in the performance of today's face recognition systems. To address this problem, we propose a novel combination of the popular triplet loss to improve robustness against image resolution via fine-tuning of existing face recognition models. With octuplet loss, we leverage the relationship between high-resolution images and their synthetically down-sampled variants jointly with their identity labels. Fine-tuning several state-of-the-art approaches with our method proves that we can significantly boost performance for cross-resolution (high-to-low resolution) face verification on various datasets without meaningfully exacerbating the performance on high-to-high resolution images. Our method applied on the FaceTransformer network achieves 95.12% face verification accuracy on the challenging XQLFW dataset while reaching 99.73% on the LFW database. Moreover, the low-to-low face verification accuracy benefits from our method. We release our code11Code available on https://github.com/Martlgap/octuplet-loss to allow seamless integration of the octuplet loss into existing frameworks.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124892814","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: 8

Activation Template Matching Loss for Explainable Face Recognition 可解释人脸识别的激活模板匹配损失

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-07-05 DOI: 10.1109/FG57933.2023.10042626

Huawei Lin, Haozhe Liu, Qiufu Li, Linlin Shen

引用次数: 0