LTP-ML: Micro-Expression Detection by Recognition of Local Temporal Pattern of Facial Movements

Li, Jingting; Soladié, Catherine; Séguier, Renaud

doi:10.1109/fg.2018.00100

Cited by 30 publications

(22 citation statements)

References 14 publications

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“…The baseline method is developed based on the proposed LTP-ML (local temporal pattern-machine learning) method in [22]. The method is extended for long videos by employing a sliding temporal window.…”

Section: B Ltp-ml: Our Proposed Baseline Methodsmentioning

confidence: 99%

“…2) Feature Extraction: In this part, local temporal patterns (LTPs) [22] are analyzed in the local region to distinguish ME from other movements. They are extracted from 12 ROIs respectively in each small sequence.…”

Section: B Ltp-ml: Our Proposed Baseline Methodsmentioning

confidence: 99%

“…Hence, the feature of frame n for ROI m j can be represented as: [CN m j , d m j (n, n + 1), · · · , d m j (n, n + W ROI − 1)], where d m j (n, n + 1) is the normalized distance value and the CN m j is the normalization coefficient. The more detailed deduction process can be found in [22]. The feature for one ROI sequence of the entire long video is the concatenation of features of all the separated sequences.…”

Section: P M Jmentioning

confidence: 99%

“…In this paper, we spot the micro-expression clips in two recently published databases, and establish the baseline method for ME spotting challenge by using directly a temporal pattern extracted from local region [22]. Frames in a ME duration are taken into account to obtain a real temporal and local pattern (LTP), and then the LTPs are recognized by a classifier.…”

Section: Introductionmentioning

confidence: 99%

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Spotting Micro-Expressions on Long Videos Sequences

Soladié

Séguier

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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This paper presents two methods for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) and local binary pattern (LBP) on two most recent databases, i.e. SAMM and CAS(ME) 2 . First we propose LTP-ML method as the baseline results for the challenge and then we compare the results with the LBP-χ 2distance method. The LTP patterns are extracted by applying PCA in a temporal window on several facial local regions. The micro-expression sequences are then spotted by a local classification of LTP and a global fusion. The LBP-χ 2 -distance method is to compare the feature difference by calculating χ 2 distance of LBP in a time window, the facial movements are then detected with a threshold. The performance is evaluated by Leave-One-Subject-Out cross validation. The overlap frames are used to determine the True Positives and the metric F1-score is used to compare the spotting performance of the databases. The F1-score of LTP-ML result for SAMM and CAS(ME) 2 are 0.0316 and 0.0179, respectively. The results show our proposed LTP-ML method outperformed LBP-χ 2 -distance method in terms of F1-score on both databases.

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Section: B Ltp-ml: Our Proposed Baseline Methodsmentioning

confidence: 99%

Section: B Ltp-ml: Our Proposed Baseline Methodsmentioning

confidence: 99%

Section: P M Jmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spotting Micro-Expressions on Long Videos Sequences

Soladié

Séguier

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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“…Unfortunately, areas of the face that are not useful for ME analysis such as the cheek area may still be captured within the triangular regions. To address this problem, more recent methods partition the face into a few region-of-interests (ROIs) (Polikovsky et al, 2009; Polikovsky and Kameda, 2013; Liong et al, 2015, 2016b,c, 2018; Davison et al, 2016b; Li et al, 2018). The ROIs are regions that correspond to one or more FACS action units (AUs).…”

Section: Spotting 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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