Micro-expression recognition: an updated review of current trends, challenges and solutions

Goh, Kam Meng; Ng, Chee How; Lim, Li Li; Sheikh, Usman Ullah

doi:10.1007/s00371-018-1607-6

Cited by 68 publications

(31 citation statements)

References 100 publications

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“…This is so quick and spontaneous that it can hardly be noticed with the naked eye. The main challenge [ 2 ] is that micro expressions are complex to identify with the naked eye, yet no one can hide them. Figure 1 shows a sample of micro expressions of negative feelings.…”

Section: Introductionmentioning

confidence: 99%

LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network

Hashmi

Ashish²,

Sharma

et al. 2021

Sensors

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Facial micro expressions are brief, spontaneous, and crucial emotions deep inside the mind, reflecting the actual thoughts for that moment. Humans can cover their emotions on a large scale, but their actual intentions and emotions can be extracted at a micro-level. Micro expressions are organic when compared with macro expressions, posing a challenge to both humans, as well as machines, to identify. In recent years, detection of facial expressions are widely used in commercial complexes, hotels, restaurants, psychology, security, offices, and education institutes. The aim and motivation of this paper are to provide an end-to-end architecture that accurately detects the actual expressions at the micro-scale features. However, the main research is to provide an analysis of the specific parts that are crucial for detecting the micro expressions from a face. Many states of the art approaches have been trained on the micro facial expressions and compared with our proposed Lossless Attention Residual Network (LARNet) approach. However, the main research on this is to provide analysis on the specific parts that are crucial for detecting the micro expressions from a face. Many CNN-based approaches extracts the features at local level which digs much deeper into the face pixels. However, the spatial and temporal information extracted from the face is encoded in LARNet for a feature fusion extraction on specific crucial locations, such as nose, cheeks, mouth, and eyes regions. LARNet outperforms the state-of-the-art methods with a slight margin by accurately detecting facial micro expressions in real-time. Lastly, the proposed LARNet becomes accurate and better by training with more annotated data.

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Section: Introductionmentioning

confidence: 99%

LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network

Hashmi

Ashish²,

Sharma

et al. 2021

Sensors

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“…An important role is also played by facial motion in emotion perception and recognition. By providing unique information about the direction, quality, and speed of motion, dynamic stimuli enhance coherence in the identification of affect, lead to stronger emotion judgments, and facilitate the differentiation between posed and spontaneous expressions ( Krumhuber et al, 2017 ; Oh et al, 2018 ; Goh et al, 2020 ). In this regard, also new technologies have introduced important innovations in face detection and recognition ( Canedo and Neves, 2019 ).…”

Section: Introduction: Facial Expression Production and Recognition As A Ict Challengementioning

confidence: 99%

How to Induce and Recognize Facial Expression of Emotions by Using Past Emotional Memories: A Multimodal Neuroscientific Algorithm

Balconi

Fronda

2021

Front. Psychol.

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“…In the last few years, non verbal communication has gained interest in different fields, such as forensic investigation, security devices, clinical diagnosis, marketing investigation and forecasting, etc. In this context, facial micro-expressions (MEs) play a fundamental role, as they reveal the actual internal emotional states and intentions of a subject [ 1 , 2 ]. Despite a fast development of both computer algorithms and video acquisition technologies, automatic ME detection is still a challenging goal [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Fast Preprocessing Method for Micro-Expression Spotting via Perceptual Detection of Frozen Frames

Bruni

Vitulano

2021

J. Imaging

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This paper presents a preliminary study concerning a fast preprocessing method for facial microexpression (ME) spotting in video sequences. The rationale is to detect frames containing frozen expressions as a quick warning for the presence of MEs. In fact, those frames can either precede or follow (or both) MEs according to ME type and the subject’s reaction. To that end, inspired by the Adelson–Bergen motion energy model and the instinctive nature of the preattentive vision, global visual perception-based features were employed for the detection of frozen frames. Preliminary results achieved on both controlled and uncontrolled videos confirmed that the proposed method is able to correctly detect frozen frames and those revealing the presence of nearby MEs—independently of ME kind and facial region. This property can then contribute to speeding up and simplifying the ME spotting process, especially during long video acquisitions.

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Micro-expression recognition: an updated review of current trends, challenges and solutions

Cited by 68 publications

References 100 publications

LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network

LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network

How to Induce and Recognize Facial Expression of Emotions by Using Past Emotional Memories: A Multimodal Neuroscientific Algorithm

A Fast Preprocessing Method for Micro-Expression Spotting via Perceptual Detection of Frozen Frames

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