Review of Automatic Microexpression Recognition in the Past Decade

Zhang, Liangfei; Arandjelović, Ognjen

doi:10.3390/make3020021

Cited by 24 publications

(8 citation statements)

References 81 publications

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“…The dataset consists of 48 × 48 pixel greyscale images with about 28,709 examples for the training set. The emotions are categorized into 6 classes: Angry, Disgust, Fear, Happy [19], Sad, surprised, and Neutral. The Extended Cohn-Kanade (CK+) Dataset was released as an extension to the original CK dataset.…”

Section: Data Sets For Facial Expressionsmentioning

confidence: 99%

Facial Emotion Recognition Using Swarm Optimized Multi-Dimensional DeepNets with Losses Calculated by Cross Entropy Function

Arun¹,

Maheswaravenkatesh²,

Jayasankar³

2023

Computer Systems Science and Engineering

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The human face forms a canvas wherein various non-verbal expressions are communicated. These expressional cues and verbal communication represent the accurate perception of the actual intent. In many cases, a person may present an outward expression that might differ from the genuine emotion or the feeling that the person experiences. Even when people try to hide these emotions, the real emotions that are internally felt might reflect as facial expressions in the form of micro expressions. These micro expressions cannot be masked and reflect the actual emotional state of a person under study. Such micro expressions are on display for a tiny time frame, making it difficult for a typical person to spot and recognize them. This necessitates a place for Machine Learning, where machines can be trained to look for these micro expressions and categorize them once they are on display. The study's primary purpose is to spot and correctly classify these micro expressions, which are very difficult for a casual observer to identify. This research improves upon the accuracy of the recognition by using a novel learning technique that not only captures and recognizes multimodal facial micro expressions but also has features for aligning, cropping, and superimposing these feature frames to produce highly accurate and consistent results. A modified variant of the deep learning architecture of Convolutional Neural Networks combined with the swarm-based optimality technique of the Artificial Bee Colony Algorithm is proposed to effectively get an accuracy of more than 85% in identifying and classifying these micro expressions in contrast to other algorithms that have relatively less accuracy. One of the main aspects of processing these expressions from video or live feeds is aligning the frames homographically and identifying these concise bursts of micro expressions, which significantly increases the accuracy of the outcomes. The proposed swarm-based technique handles this in the research to precisely align and crop the subsequent frames, resulting in much superior detection rates in identifying the micro expressions when on display.

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Section: Data Sets For Facial Expressionsmentioning

confidence: 99%

Facial Emotion Recognition Using Swarm Optimized Multi-Dimensional DeepNets with Losses Calculated by Cross Entropy Function

Arun¹,

Maheswaravenkatesh²,

Jayasankar³

2023

Computer Systems Science and Engineering

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“…Although the model demonstrated a high level of accuracy in offline trials, its implementation in real-time scenarios may encounter obstacles pertaining to processing resources, latency, and the ability to adapt to dynamic, real-world conditions ( 18 , 51 ).…”

Section: Case Studymentioning

confidence: 99%

Public mental health through social media in the post COVID-19 era

Sharma,

Singh,

Shah

et al. 2023

Front. Public Health

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Social media is a powerful communication tool and a reflection of our digital environment. Social media acted as an augmenter and influencer during and after COVID-19. Many of the people sharing social media posts were not actually aware of their mental health status. This situation warrants to automate the detection of mental disorders. This paper presents a methodology for the detection of mental disorders using micro facial expressions. Micro-expressions are momentary, involuntary facial expressions that can be indicative of deeper feelings and mental states. Nevertheless, manually detecting and interpreting micro-expressions can be rather challenging. A deep learning HybridMicroNet model, based on convolution neural networks, is proposed for emotion recognition from micro-expressions. Further, a case study for the detection of mental health has been undertaken. The findings demonstrated that the proposed model achieved a high accuracy when attempting to diagnose mental health disorders based on micro-expressions. The attained accuracy on the CASME dataset was 99.08%, whereas the accuracy that was achieved on SAMM dataset was 97.62%. Based on these findings, deep learning may prove to be an effective method for diagnosing mental health conditions by analyzing micro-expressions.

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“…Practical applications include identification, access control, and human–computer interactions. In one of the most recent studies, Zhang et al target recognition of microexpression on the human face [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

A Novel Methodology for Measuring the Abstraction Capabilities of Image Recognition Algorithms

2021

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Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.

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Review of Automatic Microexpression Recognition in the Past Decade

Cited by 24 publications

References 81 publications

Facial Emotion Recognition Using Swarm Optimized Multi-Dimensional DeepNets with Losses Calculated by Cross Entropy Function

Facial Emotion Recognition Using Swarm Optimized Multi-Dimensional DeepNets with Losses Calculated by Cross Entropy Function

Public mental health through social media in the post COVID-19 era

A Novel Methodology for Measuring the Abstraction Capabilities of Image Recognition Algorithms

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