Pu, G.; Chen, J.; Wang, R.; Tang, Z. A Lightweight Shallow Convolutional SNN Combined with STDP Fine-Tuning for Facial Expression Recognition. Preprints2024, 2024072153. https://doi.org/10.20944/preprints202407.2153.v1
APA Style
Pu, G., Chen, J., Wang, R., & Tang, Z. (2024). A Lightweight Shallow Convolutional SNN Combined with STDP Fine-Tuning for Facial Expression Recognition. Preprints. https://doi.org/10.20944/preprints202407.2153.v1
Chicago/Turabian Style
Pu, G., Rui Wang and Zhongcai Tang. 2024 "A Lightweight Shallow Convolutional SNN Combined with STDP Fine-Tuning for Facial Expression Recognition" Preprints. https://doi.org/10.20944/preprints202407.2153.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 hyperparameters, 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 the 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, Computer Vision and Graphics
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.
