Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU
Abstract
Facial Expression Recognition (FER) plays an important role in human-computer interactions and is used in a wide range of applications. Convolutional Neural Networks (CNN) have shown promise in their ability to classify human facial expressions, however, large CNNs are not well-suited to be implemented on resource- and energy-constrained IoT devices. In this work, we present a hierarchical framework for developing and optimizing hardware-aware CNNs tuned for deployment at the edge. We perform a comprehensive analysis across various edge AI accelerators including NVIDIA Jetson Nano, Intel Neural Compute Stick, and Coral TPU. Using the proposed strategy, we achieved a peak accuracy of 99.49% when testing on the CK+ facial expression recognition dataset. Additionally, we achieved a minimum inference latency of 0.39 milliseconds and a minimum power consumption of 0.52 Watts.
Cite
@article{arxiv.2305.15422,
title = {Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU},
author = {Mohammadreza Mohammadi and Heath Smith and Lareb Khan and Ramtin Zand},
journal= {arXiv preprint arXiv:2305.15422},
year = {2023}
}