Cost-Effective CNNs for Real-Time Micro-Expression Recognition
2020
Micro-Expression (ME) recognition is a hot topic in computer vision as it presents a gateway to capture and understand human's daily emotions. It is nonetheless a challenging problem due to the fact ME typically being transient (lasting less than 200 ms) and subtle. Recent advances in machine learning enable new and effective methods to be adopted for solving diverse computer vision tasks. In particular, the use of deep learning techniques on large datasets outperforms classical approaches based on classical machine learning which rely on hand-crafted features. Even though available datasets for spontaneous ME are scarce and much smaller, using off-the-shelf Convolutional Neural Networks (CNNs) still demonstrates satisfactory classification results. However, these networks are heavy in terms of memory consumption and computational resources. This poses great challenges when deploying CNN-based solutions in many applications such as driver's monitoring or comprehension recognition in virtual classrooms, which demand fast and accurate recognition. As these networks are initially designed for tasks of different domains, they are over-parameterized and need to be optimized for ME recognition. In this paper, we propose a new network based on the well-known ResNet18 which we optimize for ME classification in two ways. Firstly, we reduce the depth of the network by removing residual layers. Secondly, we introduce a more compact representation of optical flow used as input to the network. We present extensive experiments and demonstrate that the proposed network obtains accuracies comparable to the state-of-the-art methods while significantly reducing the necessary memory space. Our best classification accuracy reaches 60.17% on the challenging composite dataset containing 5 objectives classes. Our method takes only 24.6 ms for classifying a ME video clip (less than the occurrence time of the shortest ME which lasts 40 ms). Our CNN design is suitable for real-time embedded applications with limited memory and computing resources.
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