Version 1
: Received: 30 January 2024 / Approved: 31 January 2024 / Online: 31 January 2024 (05:48:50 CET)
How to cite:
Pu, G.; Chen, J. Facial Expression Recognition Based on Convolutional Spiking Neural Network and STDP Fine-Tune. Preprints2024, 2024012165. https://doi.org/10.20944/preprints202401.2165.v1
Pu, G.; Chen, J. Facial Expression Recognition Based on Convolutional Spiking Neural Network and STDP Fine-Tune. Preprints 2024, 2024012165. https://doi.org/10.20944/preprints202401.2165.v1
Pu, G.; Chen, J. Facial Expression Recognition Based on Convolutional Spiking Neural Network and STDP Fine-Tune. Preprints2024, 2024012165. https://doi.org/10.20944/preprints202401.2165.v1
APA Style
Pu, G., & Chen, J. (2024). Facial Expression Recognition Based on Convolutional Spiking Neural Network and STDP Fine-Tune. Preprints. https://doi.org/10.20944/preprints202401.2165.v1
Chicago/Turabian Style
Pu, G. and Jiankun Chen. 2024 "Facial Expression Recognition Based on Convolutional Spiking Neural Network and STDP Fine-Tune" Preprints. https://doi.org/10.20944/preprints202401.2165.v1
Abstract
Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperpa-rameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
