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

Mohammad Farukh Hashmi¹, B Kiran Kumar Ashish², Vivek Sharma³

¹Department of Electronics and Communication Engineering, National Institute of Technology, Warangal 506004, India.

Sensors (Basel, Switzerland)

|February 10, 2021

Summary

This summary is machine-generated.

Detecting micro expressions, brief emotional cues, is challenging. The proposed Lossless Attention Residual Network (LARNet) accurately identifies these subtle facial signals by focusing on crucial facial regions, outperforming existing methods.

Keywords:

LARNet facial micro expressions feature extraction lossless attention network microscaling level

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Area of Science:

Computer Vision
Human-Computer Interaction
Affective Computing

Background:

Facial micro expressions are involuntary, brief emotional indicators crucial for understanding true human emotions.
While macro expressions are easily masked, micro expressions offer deeper insights but are difficult for humans and machines to detect.
Facial expression detection is increasingly applied in various sectors, including security, psychology, and customer service.

Purpose of the Study:

To develop an end-to-end architecture for accurate micro-expression detection.
To analyze crucial facial regions vital for identifying micro expressions.
To propose and evaluate the Lossless Attention Residual Network (LARNet) against state-of-the-art methods.

Main Methods:

Developed the Lossless Attention Residual Network (LARNet), an architecture focusing on spatial and temporal information fusion.
Encoded crucial facial features from specific regions like the nose, cheeks, mouth, and eyes.
Compared LARNet's performance against existing Convolutional Neural Network (CNN)-based approaches.

Main Results:

LARNet effectively extracts spatial and temporal information from key facial areas.
The proposed LARNet demonstrates superior performance in accurately detecting micro expressions compared to state-of-the-art methods.
Real-time detection of micro expressions was achieved with high accuracy.

Conclusions:

LARNet provides an effective architecture for accurate micro-expression detection.
Focusing on specific crucial facial regions enhances the identification of subtle emotional cues.
Further improvements in LARNet's accuracy are achievable with increased annotated training data.