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

Face Denoising and 3D Reconstruction from A Single Depth Image 基于单深度图像的人脸去噪和三维重建

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00005

Yicheng Zhong, Yuru Pei, Peixin Li, Yuke Guo, Gengyu Ma, Meng Liu, Wei Bai, Wenhai Wu, H. Zha

{"title":"Face Denoising and 3D Reconstruction from A Single Depth Image","authors":"Yicheng Zhong, Yuru Pei, Peixin Li, Yuke Guo, Gengyu Ma, Meng Liu, Wei Bai, Wenhai Wu, H. Zha","doi":"10.1109/FG47880.2020.00005","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00005","url":null,"abstract":"The reconstruction of 3D face shapes and expressions from a single depth image obtained by a consumer depth camera is a challenging issue considering device-specific noise, the data missing, and the lack of textual constraints. In order to relieve the computationally-intensive nonlinear optimization of traditional template-fitting-based methods, we aim to build an end-to-end regression framework between a depth image and a 3D face encoded by the identity, the expression, and the pose parameters. Concerning the lack of paired depth images and 3D faces, we utilize the unsupervised CycleGAN-based network to adapt the regression model learned from the synthetic data to the real-captured noisy depth images. Instead of separate depth image denoising and 3D face inference, we present a task-specific coupled loss for end-to-end 3D face estimation. We propose a three-tier constraint for the shape consistency in the joint embedding, the depth image, and the surface space to avoid shape distortions in the unsupervised domain adaptation network. We report promising qualitative results for the task of the face denoising and 3D face reconstruction from a single depth image.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"8 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":"117256911","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

Visual Saliency Detection guided by Neural Signals 神经信号引导下的视觉显著性检测

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00068

S. Palazzo, F. Rundo, S. Battiato, D. Giordano, C. Spampinato

引用次数: 7

Beyond MAGIC: Matching Collaborative Gestures using an optimization-based Approach 超越MAGIC:使用基于优化的方法匹配协作手势

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00044

Edgar Rojas-Muñoz, J. Wachs

{"title":"Beyond MAGIC: Matching Collaborative Gestures using an optimization-based Approach","authors":"Edgar Rojas-Muñoz, J. Wachs","doi":"10.1109/FG47880.2020.00044","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00044","url":null,"abstract":"Gestures are a key aspect of communication during collaboration: through gestures we can express ideas, inquires and formalize instructions as we collaborate. Nevertheless, gesture analysis is not currently used to assess quality of task collaboration. One possible reason for this is that there is no consensus on how to represent and compare gestures from the semantic standpoint. To address this, this paper introduces three novel approaches to compare gestures performed by individuals as they collaborate to complete a physical task. Our approach relies on solving three variations of an integer optimization assignment problem, i.e. based on gesture similarity, based on temporal synchrony, and based on a combination of both. We collected the gestures of 40 participants (divided into 20 pairs) as they performed two collaborative tasks, and generated a human baseline that compared and matched their gestures. Afterwards, our gesture comparison approach was evaluated against other gestures comparison approaches based on how well they replicated the human baseline. Our approach outperformed the other approaches, agreeing with the human baseline over 85% of the times. Thus, the obtained results support the proposed technique for gesture comparison. This in turn can lead to the development of better methods to evaluate collaborative physical tasks.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"391 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":"116329409","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

Three-level Training of Multi-Head Architecture for Pain Detection 多头部结构疼痛检测的三级训练

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00071

Saandeep Aathreya Sidhapur Lakshminarayan, Saurabh Hinduja, Shaun J. Canavan

引用次数: 1

Adaptive Mask for Region-based Facial Micro-Expression Recognition 基于区域的面部微表情识别的自适应掩码

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00067

Walied Merghani, Moi Hoon Yap

{"title":"Adaptive Mask for Region-based Facial Micro-Expression Recognition","authors":"Walied Merghani, Moi Hoon Yap","doi":"10.1109/FG47880.2020.00067","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00067","url":null,"abstract":"Facial micro-expression can be characterized by its short duration and subtle movements. In facial micro-expression recognition, these subtle movements require more specific feature descriptors due to only a few parts of the face produce information that helps us to recognize micro-expressions. Over the past decade, researchers designed different Region of Interests (ROIs) to study specific face regions in micro-expressions recognition. To further study this aspect, we proposed a region-based method with an adaptive mask for facial micro-expression recognition. Based on the most frequent Action Units on the two publicly available datasets, i.e. CASME II and SAMM, 14 ROIs are defined where the adaptive mask is created by calculating the optical flow after Gaussian smoothing. Further, LBP-TOP features are extracted from each ROIs and Sequential Minimal Optimization is used to classify the micro-expressions. When evaluating our proposed method on CASME II and SAMM, we achieved the accuracy of 68.2% and 56.1%. In terms of F1-Score, our proposed method achieved 0.57 on CASMEII and the best performance of 0.50 on SAMM.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"184 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":"134150635","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

Exploring Multidimensional Measurements for Pain Evaluation using Facial Action Units 探索多维测量疼痛评估使用面部动作单位

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00087

Xiaojing Xu, V. D. Sa

{"title":"Exploring Multidimensional Measurements for Pain Evaluation using Facial Action Units","authors":"Xiaojing Xu, V. D. Sa","doi":"10.1109/FG47880.2020.00087","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00087","url":null,"abstract":"Although pain is widely recognized to be a multidimensional experience, it is typically measured by unidimensional patient self-reported visual analog scale (VAS). However, self-reported pain is subjective, difficult to interpret and sometimes impossible to obtain. Machine learning models have been developed to automatically recognize pain at both the frame level and sequence (or video) level. Many methods use or learn facial action units (AUs) defined by the Facial Action Coding System (FACS) for describing facial expressions with muscle movement. In this paper, we analyze the relationship between sequence-level multidimensional pain measurements and frame-level AUs and an AU derived pain-related measure, the Prkachin and Solomon Pain Intensity (PSPI). We study methods that learn sequence-level metrics from frame-level metrics. Specifically, we explore an extended multitask learning model to predict VAS from human-labeled AUs with the help of other sequence-level pain measurements during training. This model consists of two parts: a multitask learning neural network model to predict multidimensional pain scores, and an ensemble learning model to linearly combine the multidimensional pain scores to best approximate VAS. Starting from human-labeled AUs, the model achieves a mean absolute error (MAE) on VAS of 1.73. It outperforms provided human sequence-level estimates which have an MAE of 1.76. Combining our machine learning model with the human estimates gives the best performance of MAE on VAS of 1.48.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"11 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":"124742639","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

Introduction and Analysis of an Event-Based Sign Language Dataset 基于事件的手语数据集的介绍与分析

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00069

Ajay Vasudevan, Pablo Negri, B. Linares-Barranco, T. Serrano-Gotarredona

{"title":"Introduction and Analysis of an Event-Based Sign Language Dataset","authors":"Ajay Vasudevan, Pablo Negri, B. Linares-Barranco, T. Serrano-Gotarredona","doi":"10.1109/FG47880.2020.00069","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00069","url":null,"abstract":"Human gestures recognition is a complex visual recognition task where motion across time distinguishes the type of action. Automatic systems tackle this problem using complex machine learning architectures and training datasets. In recent years, the use and success of robust deep learning techniques was compatible with the availability of a great number of these sets. This paper presents SL-Animals-DVS, an event-based action dataset captured by a Dynamic Vision Sensor (DVS). The DVS records humans performing sign language gestures of various animals as a continuous spike flow at very low latency. This is especially suited for sign language gestures which are usually made at very high speeds. We also benchmark the recognition performance on this data using two state-of-the-art Spiking Neural Networks (SNN) recognition systems. SNNs are naturally compatible to make use of the temporal information that is provided by the DVS where the information is encoded in the spike times. The dataset has about 1100 samples of 58 subjects performing 19 sign language gestures in isolation at different scenarios, providing a challenging evaluation platform for this emerging technology.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"23 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":"124893764","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: 15

Real-time Action Unit Intensity Detection 实时动作单元强度检测

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00026

Saurabh Hinduja, Shaun J. Canavan

引用次数: 0

A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions 面部表情疼痛强度识别的课程学习方法

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00083

Adria Mallol-Ragolta, Shuo Liu, N. Cummins, Björn Schuller

{"title":"A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions","authors":"Adria Mallol-Ragolta, Shuo Liu, N. Cummins, Björn Schuller","doi":"10.1109/FG47880.2020.00083","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00083","url":null,"abstract":"The high prevalence of chronic pain in society raises the need to develop new digital tools that can automatically and objectively assess pain intensity in individuals. These tools can contribute to an optimisation of clinical resources, as they offer cost-effective solutions for early detection, continuous monitoring, and treatment personalisation by utilising Artificial Intelligence techniques. In this work, we present our contribution to the Pain Intensity Estimation from Facial Expressions task of the EMOPAIN 2020 Challenge. Specifically, we compare the performance of Recurrent Neural Networks trained with standard or Curriculum Learning (CL) approaches to predict the pain intensity level of individuals reported in an 11-point scale from facial expressions. The results obtained using the test partition support the use of CL-based approaches in the automatic prediction of pain from facial features. The best model trained using a CL approach achieved a Concordance Correlation Coefficient (CCC) of 0.196 in the test partition, while the model trained using a standard approach, without CL, achieved a CCC of 0.174. In terms of CCC, these results respectively represent an improvement of 0.136 and 0.114 on the best results of the baseline system reported by the Challenge organisers using the test partition.","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":"129269875","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

M3F: Multi-Modal Continuous Valence-Arousal Estimation in the Wild M3F:野外多模态连续价唤醒估计

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00098

Yuanhang Zhang, Rulin Huang, Jiabei Zeng, S. Shan

引用次数: 13