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Updated: Jun 17, 2025

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Memristive patch attention neural network for facial expression recognition and edge computing.

Kechao Zheng¹, Yue Zhou^1,2, Shukai Duan^1,2

¹College of Artificial Intelligence, Southwest University, Chongqing, 400715 China.

Cognitive Neurodynamics

|August 6, 2024

Summary

This summary is machine-generated.

This study introduces a novel network for facial expression recognition, enhancing convolutional neural networks (CNNs) by incorporating attention mechanisms to capture crucial low-level features. The proposed model achieves high accuracy on multiple datasets and offers a hardware-friendly design for edge computing.

Keywords:

Attention mechanism Facial expression recognition Memristive edge computing Patch attention

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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Convolutional Neural Networks (CNNs) have advanced facial expression recognition.
Deeper CNN models often lose critical low-level facial features, hindering recognition accuracy.
Dependencies between low-level facial features are often overlooked in current models.

Purpose of the Study:

To propose a novel CNN-based network for facial expression recognition.
To address the loss of low-level features in deep CNNs.
To improve the accuracy of facial expression recognition by capturing long-range dependencies.

Main Methods:

Introduced multiple attention mechanisms to extract long-range dependencies of low-level features.
Designed a patch attention mechanism to capture dependencies between low-level facial expression features.
Integrated the Convolutional Block Attention Module (CBAM) into the backbone network.

Main Results:

Achieved competitive accuracy on CK+ (98.10%), JAFFE (95.12%), and FER2013 (73.50%) datasets.
Demonstrated improved feature extraction ability through attention mechanisms.
Proposed a hardware-friendly implementation scheme using memristor crossbars.

Conclusions:

The novel network effectively captures low-level facial features, improving recognition accuracy.
The proposed approach offers a promising solution for facial expression recognition in edge computing.
The hardware implementation scheme facilitates efficient deployment on personal and wearable electronic devices.