Abstract
Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for real-time facial affective computing tasks. We propose a light-weight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.
Highlights
Facial affect plays a crucial role in our daily lives such as psychological analysis, medical diagnosis, education, decision-making, customer marketing, and advertising
The aim of this paper is to investigate the possibility of convolutional neural networks (CNNs) embedded in mobile devices for real-time facial affective computing under real-world scenarios
We have evaluated the proposed light-weight CNN by comparing its performance with the state-of-the-art methods including traditional methods and deep learning-based methods
Summary
Facial affect plays a crucial role in our daily lives such as psychological analysis, medical diagnosis, education, decision-making, customer marketing, and advertising. Driven by the vast application demands, facial affective computing has become an active research field and has attracted a lot of research attention from various research areas such as human-computer interaction, computer vision and artificial intelligence. Facial affective computing is one of the most important components of human-computer interaction, because it provides a new dimension to human-machine interactions. If robots can analyze human facial affect, they can have appropriate responses and behaviors according to the analysis results. In the field of psychology, affect is a term for the external exhibition of internal emotions and feelings. Facial affect is usually described based on two types of models: one is the categorical model, namely facial expressions, such as the six basic facial expressions (Happiness, Sadness, Fear, Anger, Surprise and Disgust)
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