Work

Shallow convolution and attention-based models for micro-expression recognition

Year: 2025

Type: conference-paper

Abstract: The use of deep learning models for micro-expression recognition is challenging because of the absence of large datasets.This paper proposes the construction of shallow models to address this problem.It explores block-wise 3D convolutions, 2D convolutions over frames and 2D convolutional long short-term memory (ConvLSTM) over the video and combines them with multi-headed self-attention to obtain three different models.The evaluation indicates that the proposed 2D ConvLSTM and attention-based model performs the best, beating the state-of-the-art by obtaining an accuracy of 74%.It also has a parameter size that is 10 times smaller than the state-of-the-art.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-68

Authors: Tanmay Verlekar, Prateek Upadhya

Institutions: Birla Institute of Technology and Science, Pilani - Goa Campus, Birla Institute of Technology and Science, Pilani

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: bronze

OpenAlex ID: W4409455422

FWCI:

Cites: 9

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Related to: 10