2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)最新文献_第2页

Spatio-temporal fusion for Macro- and Micro-expression Spotting in Long Video Sequences 基于时空融合的长视频序列宏、微表情识别

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00037

Li-Wei Zhang, Jingting Li, Su-Jing Wang, Xianhua Duan, Wen-Jing Yan, Hai-Yong Xie, Shu-Cheng Huang

引用次数: 39

FT-RCNN: Real-time Visual Face Tracking with Region-based Convolutional Neural Networks FT-RCNN:基于区域卷积神经网络的实时视觉人脸跟踪

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00040

Yiming Lin, Jie Shen, Shiyang Cheng, M. Pantic

{"title":"FT-RCNN: Real-time Visual Face Tracking with Region-based Convolutional Neural Networks","authors":"Yiming Lin, Jie Shen, Shiyang Cheng, M. Pantic","doi":"10.1109/FG47880.2020.00040","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00040","url":null,"abstract":"Visual face tracking is one of the most important components for face analysis in mobile applications and in video surveillance systems. In this paper, we propose an efficient face tracker called FT-RCNN, short for Face Tracking with Region-based CNN, that is based on the Faster-RCNN framework. A simple yet effective tracking branch is proposed to enable the framework to jointly perform face detection and tracking. To address the problem of insufficient training data for face tracking, we propose a novel pairwise training strategy that enables us to train face tracker by leveraging existing face detection datasets, thus eliminating the need to collect and annotate video data specifically for face tracking. Furthermore, we devise a novel loss function, termed Pair-hard Triplet Cosine Loss, that employs a pair-hard triplet mining strategy to increase the discriminative power of our face tracker. We evaluated the proposed tracker on popular video face datasets including MobiFace, ChokePoint and YouTube Face. The results have shown that FT-RCNN outperforms state-of-the-art face trackers and runs at real-time speed. We plan to release the source code for reproducible results in the future.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128137716","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

Learning privacy-enhancing face representations through feature disentanglement 通过特征解缠学习增强隐私的人脸表征

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00007

Blaž Bortolato, Marija Ivanovska, Peter Rot, J. Krizaj, Philipp Terhörst, N. Damer, P. Peer, Vitomir Štruc

{"title":"Learning privacy-enhancing face representations through feature disentanglement","authors":"Blaž Bortolato, Marija Ivanovska, Peter Rot, J. Krizaj, Philipp Terhörst, N. Damer, P. Peer, Vitomir Štruc","doi":"10.1109/FG47880.2020.00007","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00007","url":null,"abstract":"Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting compact and discriminative face representations (templates) from images in automatic face recognition systems. Due to the characteristics of CNN models, the generated representations typically encode a multitude of information ranging from identity to soft-biometric attributes, such as age, gender or ethnicity. However, since these representations were computed for the purpose of identity recognition only, the soft-biometric information contained in the templates represents a serious privacy risk. To mitigate this problem, we present in this paper a privacy-enhancing approach capable of suppressing potentially sensitive soft-biometric information in face representations without significantly compromising identity information. Specifically, we introduce a Privacy-Enhancing Face-Representation learning Network (PFRNet) that disentangles identity from attribute information in face representations and consequently allows to efficiently suppress soft-biometrics in face templates. We demonstrate the feasibility of PFRNet on the problem of gender suppression and show through rigorous experiments on the CelebA, Labeled Faces in the Wild (LFW) and Adience datasets that the proposed disentanglement-based approach is highly effective and improves significantly on the existing state-of-the-art.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131791739","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: 28

Noise Robust Hard Example Mining for Human Detection with Efficient Depth-Thermal Fusion 基于深度-热融合的噪声鲁棒硬例挖掘人体检测

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00121

Zijian Zhao, Jie Zhang, S. Shan

引用次数: 1

Towards a visual Sign Language dataset for home care services 面向家庭护理服务的视觉手语数据集

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00099

D. Kosmopoulos, I. Oikonomidis, C. Constantinopoulos, N. Arvanitis, K. Antzakas, A. Bifis, G. Lydakis, A. Roussos, A. Argyros

引用次数: 3

End-to-end Spatial Attention Network with Feature Mimicking for Head Detection 基于特征模拟的头部检测端到端空间注意网络

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00072

Junjie Zhang, Yuntao Liu, Rongchun Li, Y. Dou

{"title":"End-to-end Spatial Attention Network with Feature Mimicking for Head Detection","authors":"Junjie Zhang, Yuntao Liu, Rongchun Li, Y. Dou","doi":"10.1109/FG47880.2020.00072","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00072","url":null,"abstract":"Human head detection is a widely used task and suitable for identifying persons in practical applications. Although existing methods have achieved significant progress, the problems of false alarm and miss detection are still challenging, which arise from weak classification power of detector in the face of variability in occlusion, illumination, etc. In this paper, we present an effective end-to-end head detector called Spatial Attention Network with feature Mimicking(SANM) that can obtain better feature and enhanced classification power, through attention mechanism and a feature mimic method. The spatial-wise attention is extracted from several levels of feature and supervised by the bounding-box annotated heat map. The attention improves the quality of the features in the head and opposite area. To further improve the classification ability, we utilize the feature mimicking method to drive network learning the feature refined by a deep cascading classifier. Compared with the baseline model, our method achieves better performance and produces leading results on head detection benchmarks.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122128424","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

An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis 面向情感分析的基于脑电图的多模态面部动作数据库

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00050

Xiaotian Li, Xiang Zhang, Huiyuan Yang, Wenna Duan, W. Dai, L. Yin

{"title":"An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis","authors":"Xiaotian Li, Xiang Zhang, Huiyuan Yang, Wenna Duan, W. Dai, L. Yin","doi":"10.1109/FG47880.2020.00050","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00050","url":null,"abstract":"Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes. One behavioral change is facial expression, which has been studied extensively over the past few decades. Facial behavior varies with a person’s emotion according to differences in terms of culture, personality, age, context, and environment. In recent years, physiological activities have been used to study emotional responses. A typical signal is the electroencephalogram (EEG), which measures brain activity. Most of existing EEG-based emotion analysis has overlooked the role of facial expression changes. There exits little research on the relationship between facial behavior and brain signals due to the lack of dataset measuring both EEG and facial action signals simultaneously. To address this problem, we propose to develop a new database by collecting facial expressions, action units, and EEGs simultaneously. We recorded the EEGs and face videos of both posed facial actions and spontaneous expressions from 29 participants with different ages, genders, ethnic backgrounds. Differing from existing approaches, we designed a protocol to capture the EEG signals by evoking participants’ individual action units explicitly. We also investigated the relation between the EEG signals and facial action units. As a baseline, the database has been evaluated through the experiments on both posed and spontaneous emotion recognition with images alone, EEG alone, and EEG fused with images, respectively. The database will be released to the research community to advance the state of the art for automatic emotion recognition.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125051432","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: 6

The Ability to Recognize Microexpression and Detect Deception in the Elderly 老年人识别微表情和识破欺骗的能力

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00017

Liping He, Xunbing Shen, Zhencai Chen, Keding Li, Zhennan Liu, Ruirui Zhuo

{"title":"The Ability to Recognize Microexpression and Detect Deception in the Elderly","authors":"Liping He, Xunbing Shen, Zhencai Chen, Keding Li, Zhennan Liu, Ruirui Zhuo","doi":"10.1109/FG47880.2020.00017","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00017","url":null,"abstract":"The ability to read other people’s emotions among older people decline with age, which may result in weakened ability to detect deception. Microexpression is closely related to deception; and microexpression can be a valid cue to deception. Therefore, we hypothesized that there existed a positive correlation between microexpression recognition and deception detection in the older adults. To test the hypothesis, twenty-eight older adults (14 females, mean age=70 years, SD=6.2 years) were recruited for this study. They completed the tasks of the microexpression recognition and deception detection. The results showed that: (1) there was a positive correlation between fear macroexpression recognition and deception detection in the elderly (r=0.416, p =.028); (2) the ability of the elderly to detect truth (i.e., truth detection, mean accuracy=0.53) was significantly higher than the ability to detect deception (lie detection), t$(27)$=2.856, p=0.008; (3) there was significant main effect of type of emotional expressions$(F(2,25)$=11.721, p<.001), specifically the accuracy of recognizing happiness was superior to every other emotions’ accuracies of recognition; and (4) there were also significant differences between macroexpressions and microexpressions recognitions among the elderly $(F(1,26)$=113.918, p<.001). The results suggested that, to some degree, there was a positive correlation between the ability to recognize expressions and detecting deception in the elderly, and some factors will modulate the relationship.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114934992","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

Image-based Pose Representation for Action Recognition and Hand Gesture Recognition 基于图像的姿态表示用于动作识别和手势识别

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00066

Zeyi Lin, Wei Zhang, Xiaoming Deng, Cuixia Ma, Hongan Wang

{"title":"Image-based Pose Representation for Action Recognition and Hand Gesture Recognition","authors":"Zeyi Lin, Wei Zhang, Xiaoming Deng, Cuixia Ma, Hongan Wang","doi":"10.1109/FG47880.2020.00066","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00066","url":null,"abstract":"In this paper, we propose an effective and compact image-based pose representation named Poseimage Pyramid, which encodes the spatial and temporal information of human pose or hand pose as an image pyramid. Poseimage is constructed by the normalized distance between pairwise joints, and it has the advantage of its invariant to similarity transformations. With our Poseimage representation we can design the pose based action recognition or hand gesture recognition model using existing image or video classification models. In order to adapt to different actions with a variety of movement speed, we design Poseimage Pyramid to encode the multi-scale temporal information of human pose or hand pose. Experiments demonstrate that our pose representation is effective, and we achieve state-of-the-art performance on the action recognition datasets and the hand gesture recognition datasets. Our pose presentation is also complementary to video and optical flow streams in the seminal action recognition network I3D, and we achieve the state-of-the-art performance on the JHMDB, HMDB and UCF101 datasets by integrating our pose representation with I3D.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115386931","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

Multimodality Pain and related Behaviors Recognition based on Attention Learning 基于注意学习的多模态疼痛及相关行为识别

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00034

V. Huynh, Hyung-Jeong Yang, Gueesang Lee, Soohyung Kim

引用次数: 2