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cross-database micro-expression recognition: A Benchmark

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第2期34卷 544-559页

作者： Zhang, Tong Zong, Yuan Zheng, Wenming Chen, C. L. Philip Hong, Xiaopeng Tang, Chuangao Cui, Zhen Zhao, Guoying South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Southeast Univ Sch Biol Sci & Med Engn Lab Child Dev & Learning Sci Minist Educ Nanjing 210096 Peoples R China Xi An Jiao Tong Univ Xian 710049 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu 90016 Finland

cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis. CDMER is more challenging than the conventional micro-expression recognition (MER), because the training and testing samples in CDMER come from different micro-expression databases, resulting in inconsistency of the feature distributions between the training and testing sets. In this paper, we contribute to this topic from three aspects. First, we establish a CDMER experimental evaluation protocol aiming to allow the researchers to conveniently work on this topic and evaluate their proposed methods under the same standard. Second, we conduct benchmark experiments by using NINE state-of-the-art domain adaptation (DA) methods and SIX popular spatiotemporal descriptors for investigating CDMER problem from two different perspectives. Third, we propose a novel DA method called region selective transfer regression (RSTR) to deal with the CDMER task. The overall superior performance of RSTR over the state-of-the-art DA methods demonstrates that taking into consideration the facial local region information used in RSTR contributes to developing effective DA methods for dealing with CDMER problem.

关键词： databases Emotion recognition Protocols Training Cameras Feature extraction Task analysis cross-database micro-expression recognition micro-expression recognition domain adaptation transfer learning spatiotemporal descriptors

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cross-database micro-expression recognition based on transfer double sparse learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第30期81卷 43513-43530页

作者： Liu, Jiateng Zong, Yuan Zheng, Wenming Southeast Univ Key Lab Child Dev & Learning Sci Minist Educ Sch Biol Sci & Med Engn Nanjing 210096 Peoples R China

In recent years, the cross-database micro-expression problem has become a research hotspot in the affective computing and multimedia areas due to its vital role in analyzing human behavior and potential valuable application such as criminal investigation, lie detection and education which are closely associated with multimedia. Unlike common micro-expression recognition problem, cross-database micro-expression conducts micro-expression recognition using a database as training set (source database) while another database as testing set (target database), which is more challenging than common micro-expression recognition issue since it has a serious inconsistency of feature distribution between source database and target database. To handle the crucial cross-database micro-expression issue, a novel transfer double sparse learning method is proposed in this paper. The advantage of the proposed transfer double sparse learning model is that it can select the features and facial regions which have contributions to the cross-database micro-expression problem efficiently while further refining their corresponding features according to the importance of these features in cross-database micro-expression. Extensive experiments on three widely used micro-expression databases show that the proposed transfer double sparse learning model gets the best performance than other state-of-the-art methods. Specially, transfer double sparse learning model achieves which proves that the proposed it can cope with the cross-database micro-expression problem efficiently since it successfully refines the facial features and bridged the emotion gaps between different domains.

关键词： Transfer learning cross-database micro-expression recognition Domain adaptation

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Adaptive spatio-temporal attention neural network for cross-database micro-expression recognition

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Virtual Reality & Intelligent Hardware 2023年第2期5卷 142-156页

作者： Yuhan RAN Key Laboratory of Child Development and Learning Science of Ministry of Education Research Center for Learning ScienceSoutheast UniversityNanjing 210096China School of Cyber Science and Engineering Southeast UniversityNanjing 210096China

Background The use of micro-expression recognition to recognize human emotions is one of the most critical challenges in human-computer interaction applications. In recent years, cross-database micro-expression recognition(CDMER) has emerged as a significant challenge in micro-expression recognition and analysis. Because the training and testing data in CDMER come from different micro-expression databases, CDMER is more challenging than conventional micro-expression recognition. Methods In this paper, an adaptive spatio-temporal attention neural network(ASTANN) using an attention mechanism is presented to address this challenge. To this end, the micro-expression databases SMIC and CASME II are first preprocessed using an optical flow approach,which extracts motion information among video frames that represent discriminative features of *** preprocessing, a novel adaptive framework with a spatiotemporal attention module was designed to assign spatial and temporal weights to enhance the most discriminative features. The deep neural network then extracts the cross-domain feature, in which the second-order statistics of the sample features in the source domain are aligned with those in the target domain by minimizing the correlation alignment(CORAL) loss such that the source and target databases share similar distributions. Results To evaluate the performance of ASTANN, experiments were conducted based on the SMIC and CASME II databases under the standard experimental evaluation protocol of CDMER. The experimental results demonstrate that ASTANN outperformed other methods in relevant crossdatabase tasks. Conclusions Extensive experiments were conducted on benchmark tasks, and the results show that ASTANN has superior performance compared with other approaches. This demonstrates the superiority of our method in solving the CDMER problem.

关键词： cross-database micro-expression recognition Deep learning Attention mechanism Domain adaption

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Discriminative Region Transfer Network for cross-database micro-expression recognition

Discriminative Region Transfer Network for Cross-Database Mi...

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IEEE International Conference on Communications (ICC)

作者： Li, Jianbang Hu, Ruimin Mukherjee, Mithun Wuhan Univ Sch Comp Sci Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Compared to conventional micro-expression analysis, cross-database micro-expression recognition (CDMER) considers a more practical scenario, where the training and testing samples come from different databases. Under this problem setting, most previous micro-expression recognition methods may suffer severe performance degradation due to dataset bias or domain shift. This makes CDMER a challenging but interesting problem. Most existing CDMER methods transfer global microexpression features without considering the different contributions of facial regions to micro-expression. To tackle this problem, we first confirm the following argument: for samples of the same category from different datasets, their discriminative facial regions are similar and relevant. Therefore, we can transfer the knowledge about discriminative facial regions learned from the source domain to the target domain directly. Based on this argument, we design a novel deep domain adaption method called discriminative region transfer network (DRTN). The DRTN uses an adversarial-based adaptation method to align the source and the target distribution in the learned feature space. Then during transfer, we focus on the discriminative facial regions by the feat of attention mechanism. We conducted extensive experiments on CASME II and SMIC datasets and achieves very competitive results. These evaluations convincingly demonstrate the effectiveness of our proposed method.

关键词： cross-database micro-expression recognition domain shift discriminative facial regions adversarial-based adaption

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cross-database micro-expression recognition: A STYLE AGGREGATED AND ATTENTION TRANSFER APPROACH

CROSS-DATABASE MICRO-EXPRESSION RECOGNITION: A STYLE AGGREGA...

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IEEE International Conference on Multimedia and Expo (IEEE ICME)

作者： Zhou, Ling Mao, Qirong Xue, Luoyang Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781538692141

Deep learning systems, such as the Residual Network (ResNet), can infer a hierarchical representation of input data that facilitates categorization. In this paper, we propose a Style Aggregated and Attention Transfer framework (SA-AT) based on ResNet for cross-database micro-expression recognition (MER). The training of SA-AT has two stages. In the first stage, facial expression samples are used as the auxiliary database to train a ResNet teacher model. To benefit from the significant classification ability of teacher model which is trained on large scale data, in the second step, the attention of the teacher model is transferred to train the student model on style aggregated micro-expression databases with limited samples. Our experiments demonstrate that compared with the baseline method in micro-expression Grand Challenge 2019, our proposed technique achieves more promising performance.

关键词： cross-database micro-expression recognition micro-expression recognition transfer learning

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cross-database micro-expression recognition: A Benchmark 19

Cross-Database Micro-Expression Recognition: A Benchmark

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ACM International Conference on Multimedia Retrieval (ICMR)

作者： Zong, Yuan Zheng, Wenming Hong, Xiaopeng Tang, Chuangao Cui, Zhen Zhao, Guoying Southeast Univ Sch Biol Sci & Med Engn Nanjing Jiangsu Peoples R China Southeast Univ Minist Educ Key Lab Child Dev & Learning Sci Nanjing Jiangsu Peoples R China Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland

ISBN: (纸本)9781450367653

cross-database micro-expression recognition ( CDMER) is one of recently emerging and interesting problems in micro-expression analysis. CDMER is more challenging than the conventional micro-expression recognition ( MER), because the training and testing samples in CDMER come from different micro-expression databases, resulting in inconsistency of the feature distributions between the training and testing sets. In this paper, we contribute to this topic from two aspects. First, we establish a CDMER experimental evaluation protocol and provide a standard platform for evaluating their proposed methods. Second, we conduct extensive benchmark experiments by using NINE state-of-the-art domain adaptation ( DA) methods and SIX popular spatiotemporal descriptors for investigating the CDMER problem from two different perspectives and deeply analyze and discuss the experimental results. In addition, all the data and codes involving CDMER in this paper are released on our project website: http://***/cdmer.

关键词： cross-database micro-expression recognition micro-expression recognition domain adaptation transfer learning spatiotemporal descriptors

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Unsupervised cross-database micro-expression recognition Using Target-Adapted Least-Squares Regression

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2019年第7期E102D卷 1417-1421页

作者： Li, Lingyan Zhou, Xiaoyan Zong, Yuan Zheng, Wenming Chen, Xiuzhen Shi, Jingang Song, Peng Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China Southeast Univ Sch Biol Sci & Med Engn Minist Educ Key Lab Child Dev & Learning Sci Nanjing 210096 Jiangsu Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu 90014 Finland Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

Over the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.

关键词： cross-database micro-expression recognition micro-expression recognition domain adaptation transfer learning least-squares regression

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Domain Regeneration for cross-database micro-expression recognition

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第5期27卷 2484-2498页

作者： Zong, Yuan Zheng, Wenming Huang, Xiaohua Shi, Jingang Cui, Zhen Zhao, Guoying Southeast Univ Minist Educ Sch Biol Sci & Med Engn Key Lab Child Dev & Learning Sci Nanjing 210096 Jiangsu Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Faulty Informat Technol & Elect Engn FI-90014 Oulu Finland Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

Recently, micro-expression recognition has attracted lots of researchers' attention due to its potential value in many practical applications, e.g., lie detection. In this paper, we investigate an interesting and challenging problem in micro-expression recognition, i.e., cross-database micro-expression recognition, in which the training and testing samples come from different micro-expression databases. Under this problem setting, the consistent feature distribution between the training and testing samples originally existing in conventional micro-expression recognition would be seriously broken, and hence, the performance of most current well-performing micro-expression recognition methods may sharply drop. In order to overcome it, we propose a simple yet effective framework called domain regeneration (DR) in this paper. The DR framework aims at learning a domain regenerator to regenerate the micro-expression samples from source and target databases, respectively, such that they can abide by the same or similar feature distributions. Thus, we are able to use the classifier learned based on the labeled source micro-expression samples to predict the label information of the unlabeled target micro-expression samples. To evaluate the proposed DR framework, we conduct extensive cross-database micro-expression recognition experiments designed based on the Spontaneous micro-expression database and Chinese Academy of Sciences micro-expression II database. Experimental results show that compared with the recent state-of-the-art cross-database emotion recognition methods, the proposed DR framework has more promising performance.

关键词： cross-database micro-expression recognition micro-expression recognition domain adaptation transfer learning

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Learning a Target Sample Re-Generator for cross-database micro-expression recognition 17

Learning a Target Sample Re-Generator for Cross-Database Mic...

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25th ACM International Conference on Multimedia (MM)

作者： Zong, Yuan Huang, Xiaohua Zheng, Wenming Cui, Zhen Zhao, Guoying Southeast Univ Res Ctr Learning Sci Minist Educ Key Lab Child Dev & Learning Sci Nanjing 210096 Jiangsu Peoples R China Univ Oulu Fac Informat Technol & Elect Engn Ctr Machine Vis & Signal Anal FI-90014 Oulu Finland Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9781450349062

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

关键词： cross-database micro-expression recognition micro-expression recognition domain adaptation transfer learning emotion recognition

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