MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images

TL;DR

This paper introduces MicroExpNet, a tiny, fast CNN for facial expression recognition, leveraging knowledge distillation and challenging assumptions about max-pooling's usefulness, achieving real-time performance with insights into model design.

Contribution

Proposes MicroExpNet, an extremely small and fast CNN for FER, and explores the effects of knowledge distillation and max-pooling on model performance and generalization.

Findings

MicroExpNet is less than 1MB and runs at 1851 fps on CPU.

Knowledge distillation significantly improves small model performance.

Max-pooling unexpectedly enhances generalization in FER models.

Abstract

This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images. To this end, we employed the popular knowledge distillation (KD) method and identified two major shortcomings with its use: 1) a fine-grained grid search is needed for tuning the temperature hyperparameter and 2) to find the optimal size-accuracy balance, one needs to search for the final network size (or the compression rate). On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size. In addition, we hypothesized that translation invariance achieved using max-pooling layers would not be useful for the FER problem as the expressions are sensitive to small, pixel-wise changes around the eye and the mouth. However, we have found an intriguing improvement on generalization when max-pooling is used. We conducted experiments on two widely-used FER datasets, CK+ and Oulu-CASIA. Our smallest model (MicroExpNet), obtained using knowledge distillation, is less than 1MB in size and works at 1851 frames per second on an Intel i7 CPU. Despite being less accurate than the state-of-the-art, MicroExpNet still provides significant insights for designing a microarchitecture for the FER problem.