Micro-Expression Recognition based on the Multi-Color ULBP and Histogram of Gradient Direction from Six Intersection Planes

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

doi:10.52547/jiaeee.19.3.123

Cited by 2 publications

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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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“…• Unlike Esmaeili and Shahdi's work [7], we automatically select a few specific planes and apply the PACF to locate the apex frame. Also, unlike [2,10,18], and [19], which consider only limited planes (four or six planes), every 15 planes that represent each direction of movement are included in our work.…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…This method improves the results in micro‐movement analysis, which is further discussed in detail in Section 4. Unlike Esmaeili and Shahdi's work [7], we automatically select a few specific planes and apply the PACF to locate the apex frame. Also, unlike [2, 10, 18], and [19], which consider only limited planes (four or six planes), every 15 planes that represent each direction of movement are included in our work. Assume an example, where the fifth frame is the ground‐truth apex referring to the database tag in a sample (e. g., CASME‐sub01‐EP16_3).…”

Section: Introductionmentioning

confidence: 99%

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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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Micro-Expression Recognition based on the Multi-Color ULBP and Histogram of Gradient Direction from Six Intersection Planes

Cited by 2 publications

References 0 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

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

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