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Communication Dans Un Congrès Année : 2023

Lightweight Attention-Based CNN on Embedded Systems for Emotion Recognition

Slavisa Jovanovic
Naeem Ramzan
Hassan Rabah

Résumé

Decoding human emotions through Facial Expression Recognition (FER) is a challenging, yet critical endeavor, particularly on resource-limited embedded systems. This research introduces a method centered around an attention-augmented Convolutional Neural Network (CNN), tailored to detect facial Action Units (AUs), which are intricate facial movements tied to distinct emotions. To optimize for resource-constrained environments, the model underwent a three-step optimization process: restructuring the CNN architecture, model pruning, and quantization. Despite its compact footprint of only 57,001 parameters, the model delivers robust performance across multiple datasets. Once these AUs are accurately identified, we utilize the Facial Action Coding System (FACS) to map these units to corresponding emotions, thereby facilitating comprehensive emotion recognition and explanation. The incorporation of quantization further refines our model without compromising its performance, enabling efficient, real-world emotion recognition even within constrained environments.
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Dates et versions

hal-04451554 , version 1 (11-02-2024)

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Citer

Mohammad Mahdi Deramgozin, Slavisa Jovanovic, Naeem Ramzan, Hassan Rabah. Lightweight Attention-Based CNN on Embedded Systems for Emotion Recognition. 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Dec 2023, Instanbul, Turkey. pp.1-4, ⟨10.1109/ICECS58634.2023.10382796⟩. ⟨hal-04451554⟩
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