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A Video Sequence Face Expression Recognition Method Based on Squeeze-and-Excitation and 3DPCA Network.

Chang Li¹, Chenglin Wen¹, Yiting Qiu²

¹School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China.

Sensors (Basel, Switzerland)

|January 21, 2023

Summary

This study introduces a novel SE-3DPCANet for 3D video expression recognition. The method enhances accuracy and reduces training time by using 3D tensor convolutions and attention mechanisms for better feature extraction.

Keywords:

3DPCANet Squeeze-and-Excitation network expression recognition

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Traditional methods struggle with 3D video expression recognition due to high dimensionality and loss of spatial-temporal information.
Convolutional Neural Networks (CNNs) often convert 3D data into vectors, increasing computational cost and reducing accuracy.

Purpose of the Study:

To develop an efficient and accurate method for 3D video face expression recognition.
To address the limitations of traditional CNNs in handling 3D spatial-temporal data.

Main Methods:

Proposed a novel SE-3DPCANet (Squeeze-Excitation and 3D Principal Component Analysis Network).
Utilized 3D Principal Component Analysis (3DPCA) for tensor convolution kernels to extract spatial-temporal features directly from video sequences.
Integrated Squeeze-Excitation (SE) Network in the feature encoding layer to learn channel feature weights automatically.

Main Results:

Achieved higher recognition rates compared to existing methods on three benchmark datasets.
Significantly reduced training time, indicating improved computational efficiency.
Demonstrated enhanced model representation capability through adaptive channel feature weighting.

Conclusions:

The SE-3DPCANet effectively extracts dynamic expression features from 3D video data.
The proposed method offers a superior balance of accuracy and efficiency for expression recognition tasks.
This approach advances human-computer interaction by enabling more accurate and faster emotion understanding.