2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)最新文献_第3页

A weightless regression system for predicting multi-modal empathy 多模态共情预测的无权重回归系统

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00086

L. L. Filho, L. Oliveira, Hugo C. C. Carneiro, Gabriel P. Guarisa, A. L. Filho, F. França, P. Lima

引用次数: 5

Using Deep 3D Features and an LSTM Based Sequence Model for Automatic Pain Detection in the Wild 使用深度3D特征和基于LSTM的序列模型进行野外自动疼痛检测

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00097

Sowmya Rasipuram, Bukka Nikhil Sai, D. Jayagopi, Anutosh Maitra

{"title":"Using Deep 3D Features and an LSTM Based Sequence Model for Automatic Pain Detection in the Wild","authors":"Sowmya Rasipuram, Bukka Nikhil Sai, D. Jayagopi, Anutosh Maitra","doi":"10.1109/FG47880.2020.00097","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00097","url":null,"abstract":"Automatic pain detection is an important problem in diagnostic and therapeutic applications. In this paper, we aim to develop a computational framework to automatically detect pain in videos in the wild. The videos in the wild vary with respect to gender, age, ethnicity and even other qualitative attributes like upbringing. Previous systems focused on methodologies confined to one particular dataset that is hard to generalize for the population in the wild, or based on invasive methods that collect data using many physiological sensors and induced stressors. We propose a method to automatically detect pain in videos using state-of-the-art expression recognition system along with deep learning. We curated a dataset of 194 videos in the wild with pain and non-pain. We used a sliding window strategy to obtain a fixed-length input sample for the LSTM (Long Short Term Memory) network. We then carefully concatenate the network output of every segment to generate a video-level output. The proposed end-to-end framework can predict binary classification label (pain/non-pain) at video level. Our method achieves promising results on the dataset we collected.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"27 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":"122363235","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

Feature Selection for Zero-Shot Gesture Recognition 零射击手势识别的特征选择

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00046

Naveen Madapana, J. Wachs

{"title":"Feature Selection for Zero-Shot Gesture Recognition","authors":"Naveen Madapana, J. Wachs","doi":"10.1109/FG47880.2020.00046","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00046","url":null,"abstract":"Existing classification techniques assign a predetermined categorical label to each sample and cannot recognize the new categories that might appear after the training stage. This limitation has led to the advent of new paradigms in machine learning such as zero-shot learning (ZSL). ZSL aims to recognize unseen categories by having a high-level description of them. While deep learning has pushed the limits of ZSL for object recognition, ZSL for temporal problems such as unfamiliar gesture recognition (ZSGL) remain unexplored. Previous attempts to address ZSGL were focused on the creation of gesture attributes, attribute-based datasets, and algorithmic improvements, and there is little or no research concerned with feature selection for ZSGL problems. It is indisputable that deep learning has obviated the need for feature engineering for the problems with large datasets. However, when the data is scarce, it is critical to leverage the domain information to create discriminative input features. The main goal of this work is to study the effect of three different feature extraction techniques (raw features, engineered features, and deep learning features) on the performance of ZSGL. Next, we propose a new approach for ZSGL that jointly minimizes the reconstruction loss, semantic and classification losses. Our methodology yields an unseen class accuracy of (38%) which parallels the accuracies obtained through state-of-the-art approaches.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"139 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":"125637848","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

Set Operation Aided Network for Action Units Detection 为动作单元检测设置操作辅助网络

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00030

Huiyuan Yang, Taoyue Wang, L. Yin

{"title":"Set Operation Aided Network for Action Units Detection","authors":"Huiyuan Yang, Taoyue Wang, L. Yin","doi":"10.1109/FG47880.2020.00030","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00030","url":null,"abstract":"As a large number of parameters exist in deepmodel based methods, training such models usually requires many fully AU-annotated facial images. This is true with regard to the number of frames in two widely used datasets: BP4D[31] and DISFA [18], while those frames were captured from a small number of subjects (41, 27 respectively). This is problematic, as subjects produce highly consistent facial muscle movements, adding more frames per subject would only adds more close points in the feature space, and thus the classifier does not benefit from those extra frames. Data augmentation methods can be applied to alleviate the problem to a certain degree, but they fail to augment new subjects. We propose a novel Set Operation Aided Network (SO-Net) for action units detection. Specifically, new features and the corresponding labels are generated by adding set operations to both the feature and label spaces. The generated new features can be treated as a representation of a hypothetical image. As a result, we can implicitly obtain training examples beyond what was originally observed in the dataset. Therefore, the deep model is forced to learn subject-independent features, and is generalizable to unseen subjects. SO-Net is end-to-end trainable, and can be flexibly plugged in any CNN model during training. We evaluate the proposed method on two public datasets, BP4D and DISFA. The experiment shows a state-of-the-art performance, demonstrating the effectiveness of the proposed method.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"64 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":"125734432","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

Spectral Correspondence Framework for Building a 3D Baby Face Model 建立三维婴儿面部模型的光谱对应框架

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00079

Araceli Morales, A. R. Porras, Liyun Tu, M. Linguraru, G. Piella, F. Sukno

{"title":"Spectral Correspondence Framework for Building a 3D Baby Face Model","authors":"Araceli Morales, A. R. Porras, Liyun Tu, M. Linguraru, G. Piella, F. Sukno","doi":"10.1109/FG47880.2020.00079","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00079","url":null,"abstract":"Early detection of facial dysmorphology - variations of the normal facial geometry - is essential for the timely detection of genetic conditions, which has a significant impact in the reduction of the mortality and morbidity associated with them. A model encoding the normal variability in the healthy population can serve as a reference to quantify the often subtle facial abnormalities that are present in young patients with such conditions. In this paper, we present the first facial model constructed exclusively from newborn data, the Baby Face Model (BabyFM). Our model is built from 3D scans with an innovative pipeline based on least squared conformal maps (LSCM). LSCM are piece-wise linear mappings that project the training faces to a common 2D space minimising the conformal distortion. This process allows improving the correspondences between 3D faces, which is particularly important for the identification of subtle dysmorphology. We evaluate the ability of our BabyFM to recover the babys facial morphology from a set of 2D images by comparing it to state-of-the-art facial models. We also compare it to models built following an analogous pipeline to the one proposed in this paper but using nonrigid iterative closest point (NICP) to establish dense correspondences between the training faces. The results show that our model reconstructs the facial morphology of babies with significantly smaller errors than the state-of-the-art models (p = 10−4) and the “NICP models” (p < 0.01).","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"17 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":"129304443","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

Summarizing Driving Behavior to Support Driver Stress Analysis 总结驾驶行为以支持驾驶员应力分析

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00058

André Roberto Ortoncelli, Luciano Silva, O. Bellon, Tiago Mota de Oliveira, Juliana Daga

{"title":"Summarizing Driving Behavior to Support Driver Stress Analysis","authors":"André Roberto Ortoncelli, Luciano Silva, O. Bellon, Tiago Mota de Oliveira, Juliana Daga","doi":"10.1109/FG47880.2020.00058","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00058","url":null,"abstract":"Several student drivers have a high level of stress and may need assistance of a specialist, such as a psychologist, in order to enhance their driving skills or even be able to drive on their own. In this context, it is advantageous that such specialists possess resources to deeply analyze and understand the student reactions when submitted to practical driving activities. The literature already include works focusing on automatic detection of driver stress and methods for assisting motorists in real time. However, there is an open research gap regarding the production of reports and summaries about driving activities that can be useful for students behavior analysis and treatment. To this end, we propose an approach for analyzing and summarizing information about driver stress based on their behavior. The approach is supported by a computational tool that allows to view different types of information about the driver under three perspectives: i) videos of the driving activities; ii) reports of behavior analysis; and iii) summaries of relevant actions. A dataset with videos, heart rate and geographic location of driving activities developed by student drivers was produced. The dataset is initially labeled, then the discrete label values are transformed to continuous values to improve visualization and summarization. The approach was evaluated qualitatively by a psychologist and driving instructors. The proposed approach helps a professional to quickly understand the drivers’ profile, interpreting the causes of the drivers’ reactions, thus providing more accurate assistance.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"220 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":"114666239","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

Two-stage Recognition and Beyond for Compound Facial Emotion Recognition 复合面部情绪识别的两阶段识别及以后

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00144

Zhiyuan Zhang, Miao Yi, Juan Xu, Rong Zhang, Jianping Shen

引用次数: 9

Recognizing Perceived Emotions from Facial Expressions 从面部表情中识别感知到的情绪

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00025

Saurabh Hinduja, Shaun J. Canavan, L. Yin

引用次数: 0

Automatic Detection of Self-Adaptors for Psychological Distress 心理困扰自适应的自动检测

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00032

Weizhe Lin, Indigo Orton, Ming-Yu Liu, M. Mahmoud

{"title":"Automatic Detection of Self-Adaptors for Psychological Distress","authors":"Weizhe Lin, Indigo Orton, Ming-Yu Liu, M. Mahmoud","doi":"10.1109/FG47880.2020.00032","DOIUrl":"https://doi.org/10.1109/FG47880.2020.00032","url":null,"abstract":"Psychological distress is a significant and growing issue in society. Automatic detection, assessment, and analysis of such distress is an active area of research. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse. This is, in part, due to the lack of available datasets and difficulty in automatically extracting useful body features. Recent advances in pose estimation and deep learning have enabled new approaches to this modality and domain. We propose a novel method to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to be correlated with psychological distress. We also propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector encoding. We also demonstrate that our proposed model, combining audio-visual features with automatically detected fidgeting behavioral cues, can successfully predict distress levels in a dataset labeled with self-reported anxiety and depression levels. To enable this research we introduce a new dataset containing full body videos for short interviews and self-reported distress labels.","PeriodicalId":238606,"journal":{"name":"2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)","volume":"14 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":"129940430","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: 4

Multimodal(Audio, Facial and Gesture) based Emotion Recognition challenge 基于多模态(音频，面部和手势)的情感识别挑战

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) Pub Date : 2020-11-01 DOI: 10.1109/FG47880.2020.00142

Wei Gou, Jian Li, Mo Sun

引用次数: 10