A Relaxed K-SVD Algorithm for Spontaneous Micro-Expression Recognition

Zheng, Hao; Geng, Xin; Yang, Zhongxue

doi:10.1007/978-3-319-42911-3_58

Cited by 15 publications

(9 citation statements)

References 8 publications

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“…In the current micro-expression domain, several works [33], [21] aimed to preserve the temporal dimension as its dynamics is crucial for recognizing facial movements. We use a popular variant of the recurrent neural network called Long Short-Term Memory (LSTM) [34] to learn the spatially-encoded sequential input, φ(x t ).…”

Section: Temporal Learningmentioning

confidence: 99%

Enriched Long-Term Recurrent Convolutional Network for Facial Micro-Expression Recognition

Khor

See

Phan

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

169

146

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Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases. Recently, handcrafted techniques have achieved superior performance in micro-expression recognition but at the cost of domain specificity and cumbersome parametric tunings. In this paper, we propose an Enriched Long-term Recurrent Convolutional Network (ELRCN) that first encodes each micro-expression frame into a feature vector through CNN module(s), then predicts the micro-expression by passing the feature vector through a Long Short-term Memory (LSTM) module. The framework contains two different network variants: (1) Channel-wise stacking of input data for spatial enrichment, (2) Feature-wise stacking of features for temporal enrichment. We demonstrate that the proposed approach is able to achieve reasonably good performance, without data augmentation. In addition, we also present ablation studies conducted on the framework and visualizations of what CNN "sees" when predicting the micro-expression classes.

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Section: Temporal Learningmentioning

confidence: 99%

Enriched Long-Term Recurrent Convolutional Network for Facial Micro-Expression Recognition

Khor

See

Phan

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

169

146

View full text Add to dashboard Cite

show abstract

“…Micro-expressions are brief facial movements characterized by short duration, involuntariness and subtle intensity. In the literature, previous methods opted for extracting hand-crafted features from texture videos such as LBP-TOP and HOOF [83]. More recently, deep learning methods were proposed to tackle the problem by applying CNNs [6], [40] and RNNs [40].…”

Section: D Facial Expression Recognitionmentioning

confidence: 99%

Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition

Tanfous

Drira

Amor

2020

IEEE Trans. Pattern Anal. Mach. Intell.

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The detection and tracking of human landmarks in video streams has gained in reliability partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying geometric data is playing an important role in the automatic human behavior understanding. However, suitable shape representations as well as their temporal evolution, termed trajectories, often lie to nonlinear manifolds. This puts an additional constraint (i.e., nonlinearity) in using conventional Machine Learning techniques. As a solution, this paper accommodates the well-known Sparse Coding and Dictionary Learning approach to study time-varying shapes on the Kendall shape spaces of 2D and 3D landmarks. We illustrate effective coding of 3D skeletal sequences for action recognition and 2D facial landmark sequences for macro-and micro-expression recognition. To overcome the inherent nonlinearity of the shape spaces, intrinsic and extrinsic solutions were explored. As main results, shape trajectories give rise to more discriminative time-series with suitable computational properties, including sparsity and vector space structure. Extensive experiments conducted on commonly-used datasets demonstrate the competitiveness of the proposed approaches with respect to state-of-the-art.

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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%

“…However, each of these methods tackle the sparseness of MEs differently. The relaxed K-SVD (Zheng et al, 2016) learns a sparse dictionary to distinguish different MEs by minimizing the variance of sparse coefficients. The SRC (Yang et al, 2012) used in Zheng (2017) represents a given test sample as a sparse linear combination of all training samples; hence the sparse nonzero representation coefficients are likely to concentrate on training samples that are of the same class as the test sample.…”

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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A Relaxed K-SVD Algorithm for Spontaneous Micro-Expression Recognition

Cited by 15 publications

References 8 publications

Enriched Long-Term Recurrent Convolutional Network for Facial Micro-Expression Recognition

Enriched Long-Term Recurrent Convolutional Network for Facial Micro-Expression Recognition

Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition

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

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