Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU
This work addresses the problem of efficient facial expression recognition for edge computing applications, though it is incremental as it focuses on optimizing existing methods for specific hardware.
The paper tackled the challenge of deploying large CNNs for facial expression recognition on resource-constrained IoT devices by developing a hardware-aware optimization framework, achieving 99.49% accuracy, 0.39 ms latency, and 0.52 W power consumption on edge accelerators.
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.