Micro-expression recognition based on local binary patterns from three orthogonal planes and nearest neighbor method

Guo, Yanjun; Tian, Yantao; Gao, Xu; Zhang, Xuange

doi:10.1109/ijcnn.2014.6889620

Cited by 33 publications

(16 citation statements)

References 12 publications

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“…Among appearance-based feature extraction methods, local binary pattern on three orthogonal planes (LBP-TOP) is widely applied in many works (Li et al, 2013; Guo et al, 2014; Le Ngo et al, 2014, 2015, 2016a,b; Yan et al, 2014a; Adegun and Vadapalli, 2016; Zheng et al, 2016; Wang et al, 2017). Most existing datasets (SMIC, CASME II, SAMM) have all reported the LBP-TOP as their baseline evaluation method.…”

Section: Recognition Of Facial Micro-expressionsmentioning

confidence: 99%

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

et al. 2018

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Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today, in contrast to decades ago when it was primarily the domain of psychiatrists where analysis was largely manual. Indeed, although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.

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Section: Recognition Of Facial Micro-expressionsmentioning

confidence: 99%

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

et al. 2018

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“…Moreover, in the person dependent experiments (fivefold cross validation), the average accuracy of 97.4% was achieved by 3D-CNNs plus transfer learning, which was more than twenty percent higher than person independent experiments (LOSO). In the results listed in Table 5, LBP-TOP + Nearest Neighbor [5] carried out person independent experiments and person dependent experiments, and the recognition accuracy of person dependent experiment was 65.8%, while the performance of person independent experiment was 53.7%.…”

Section: Smicmentioning

confidence: 99%

Combining 3D Convolutional Neural Networks with Transfer Learning by Supervised Pre-Training for Facial Micro-Expression Recognition

Zhi

Wan

et al. 2019

IEICE Trans. Inf. & Syst.

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Facial micro-expression is momentary and subtle facial reactions, and it is still challenging to automatically recognize facial micro-expression with high accuracy in practical applications. Extracting spatiotemporal features from facial image sequences is essential for facial micro-expression recognition. In this paper, we employed 3D Convolutional Neural Networks (3D-CNNs) for self-learning feature extraction to represent facial micro-expression effectively, since the 3D-CNNs could well extract the spatiotemporal features from facial image sequences. Moreover, transfer learning was utilized to deal with the problem of insufficient samples in the facial micro-expression database. We primarily pretrained the 3D-CNNs on normal facial expression database Oulu-CASIA by supervised learning, then the pre-trained model was effectively transferred to the target domain, which was the facial micro-expression recognition task. The proposed method was evaluated on two available facial micro-expression datasets, i.e. CASME II and SMIC-HS. We obtained the overall accuracy of 97.6% on CASME II, and 97.4% on SMIC, which were 3.4% and 1.6% higher than the 3D-CNNs model without transfer learning, respectively. And the experimental results demonstrated that our method achieved superior performance compared to state-of-the-art methods.

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“…Method Accuracy LBP-SIP+RBF kernel [14] 64.02% OSW-LBP-TOP+RBF kernel [15] 57.71% LBP-TOP+Nearest Neighbor [17] 65.83% DLSTD+RPCA+SVM [16] 68.29% LBP-TOP+GSLSR 70.12% LBP-TOP+RPCA+GSLSR 67.07%…”

Section: Smic Databasementioning

confidence: 99%

“…As we did in the above experiment, we extracted LBP-TOP features and the leave-one-out cross validation was applied. For comparison purpose, four works are used as the baseline methods, i.e., in addition to the research work in [14], [15] and [16], we also choose the recent work of Guo et al in [17]. Besides, our method plus RPCA scheme proposed in [16] is included in the comparison as well.…”

Section: Smic Databasementioning

confidence: 99%

Micro-Expression Recognition by Regression Model and Group Sparse Spatio-Temporal Feature Learning

Lü

Zheng

Wang

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, a micro-expression recognition method is investigated by integrating both spatio-temporal facial features and a regression model. To this end, we first perform a multi-scale facial region division for each facial image and then extract a set of local binary patterns on three orthogonal planes (LBP-TOP) features corresponding to divided facial regions of the micro-expression videos. Furthermore, we use GSLSR model to build the linear regression relationship between the LBP-TOP facial feature vectors and the micro expressions label vectors. Finally, the learned GSLSR model is applied to the prediction of the micro-expression categories for each test micro-expression video. Experiments are conducted on both CASME II and SMIC micro-expression databases to evaluate the performance of the proposed method, and the results demonstrate that the proposed method is better than the baseline micro-expression recognition method. key words: micro-expression recognition, local binary patterns on three orthogonal planes (LBP-TOP), group sparse least squares regression (GSLSR)

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Micro-expression recognition based on local binary patterns from three orthogonal planes and nearest neighbor method

Cited by 33 publications

References 12 publications

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

Combining 3D Convolutional Neural Networks with Transfer Learning by Supervised Pre-Training for Facial Micro-Expression Recognition

Micro-Expression Recognition by Regression Model and Group Sparse Spatio-Temporal Feature Learning

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