A comprehensive survey on facial micro-expression: approaches and databases

Esmaeili, Vida; Feghhi, Mahmood Mohassel; Shahdi, Seyed Omid

doi:10.1007/s11042-022-13133-2

Cited by 11 publications

(3 citation statements)

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“…LBP is one of the most efficient texture descriptors which has been exerted in many feature extraction and processing tasks [16,[36][37][38][39][40][41][42][43][44]. In [6], they apply the same feature extraction method on X-ray images for COVID-19 identification.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

Esmaeili,

Mohassel Feghhi,

Shahdi

2024

Scientia Iranica

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The contagious and pandemic COVID-19 disease is currently considered as the main health concern and posed widespread panic across human-beings. It affects the human respiratory tract and lungs intensely. So that it has imposed significant threats for premature death. Although, its early diagnosis can play a vital role in revival phase, the radiography tests with the manual intervention are a time-consuming process. Time is also limited for such manual inspecting of numerous patients in the hospitals. Thus, the necessity of automatic diagnosis on the chest X-ray or the CT images with a high efficient performance is urgent. Toward this end, we propose a novel method, named as the ULGFBP-ResNet51 to tackle with the COVID-19 diagnosis in the images. In fact, this method includes Uniform Local Binary Pattern (ULBP), Gabor Filter (GF), and ResNet51. According to our results, this method could offer superior performance in comparison with the other methods, and attain maximum accuracy.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

Esmaeili,

Mohassel Feghhi,

Shahdi

2024

Scientia Iranica

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“…Understanding one’s emotional state is pivotal in providing the responses one intends for their actions. With the advances in deep learning and sensor technologies, there has been increased attention to capturing the emotional state of the person from the facial expression 2 , 3 . Automatic facial emotion recognition has seen wide applications in scenarios where data acquisition opportunities are limited such as among individuals with intellectual disabilities, children with autism, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-source transfer learning for facial emotion recognition using multivariate correlation analysis

Sarkar,

Behera

et al. 2023

Sci Rep

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Deep learning techniques have proven to be effective in solving the facial emotion recognition (FER) problem. However, it demands a significant amount of supervision data which is often unavailable due to privacy and ethical concerns. In this paper, we present a novel approach for addressing the FER problem using multi-source transfer learning. The proposed method leverages the knowledge from multiple data sources of similar domains to inform the model on a related task. The approach involves the optimization of aggregate multivariate correlation among the source tasks trained on the source dataset, thus controlling the transfer of information to the target task. The hypothesis is validated on benchmark datasets for facial emotion recognition and image classification tasks, and the results demonstrate the effectiveness of the proposed method in capturing the group correlation among features, as well as being robust to negative transfer and performing well in few-shot multi-source adaptation. With respect to the state-of-the-art methods MCW and DECISION, our approach shows an improvement of 7% and $$\sim$$ ∼ 15% respectively.

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“…One of them is Micro-Expression (ME), in which micro-movements of facial muscles are interpreted as facial expressions. This facial micro-momentary expression is uncontrollable and involuntary [1][2][3]; hence, it divulges lure, lie, fraud, and wile. As a consequence, it can be applied as an interrogation supplement in police departments, courts, health centres etc [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Spotting micro‐movements in image sequence by introducing intelligent cubic‐LBP

Esmaeili

Feghhi

Shahdi

2022

IET Image Processing

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Invisible to naked human eyes, micro-movements are barely noticeable. There are wide ranges of micro-movement applications from spotting subtle changes in a volcano, vascular pulse, and blood vessel to micro-expression detection. In the latter, some evil thoughts can be unveiled, resulting in identifying crooks and lawbreakers and it arises from involuntary subtle and short-duration of facial muscles movements. Precise spotting of these tiny movements is possible only when multiple aspects of temporal images are scrutinized. Meanwhile, since motions often happen in one or two directions, it is rudimentary to extract complete feature sets in textural-based approaches such as cubic Local Binary Pattern (cubic-LBP). Approaches like cubic-LBP also have imposed an unnecessary computation burden. Hence, in this research, a novel method named intelligent cubic-LBP is proposed, which incorporates Convolutional Neural Network (CNN) model. This model learns to select the useful plane(s) automatically. Apex is then detected by applying Partial Autocorrelation Coefficient (PACF) on the selected plane(s). The experimental results show significant improvement in micro-movements identification. The accuracy of the apex frame detection has elevated to 10% and 17% in the Chinese Academy of Sciences Micro-Expressions (CASME) and the CASME II databases, respectively.

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A comprehensive survey on facial micro-expression: approaches and databases

Cited by 11 publications

References 100 publications

COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

Multi-source transfer learning for facial emotion recognition using multivariate correlation analysis

Spotting micro‐movements in image sequence by introducing intelligent cubic‐LBP

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