ResMoNet: A Residual Mobile-based Network for Facial Emotion Recognition in Resource-Limited Systems
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The Deep Neural Networks (DNNs) models have contributed a high accuracy for the classification of human emotional states from facial expression recognition data sets, where efficiency is an important factor for resource-limited systems as mobile devices and embedded systems. There are efficient Convolutional Neural Networks (CNN) models as MobileNet, PeleeNet, Extended Deep Neural Network (EDNN) and Inception-Based Deep Neural Network (IDNN) in terms of model architecture results: parameters, Floating-point OPerations (FLOPs) and accuracy. Although these results are satisfactory, it is necessary to evaluate other computational resources related to the trained model such as main memory utilization and response time to complete the emotion recognition. In this paper, we compare our proposed model inspired in depthwise separable convolutions and residual blocks with MobileNet, PeleeNet, EDNN and IDNN. The comparative results of the CNN architectures and the trained models –with Radboud Faces Database (RaFD)– installed in a resource-limited device are discussed.
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