Dynamic Facial Emotion Recognition Using Deep Spatial Feature and Handcrafted Spatiotemporal Feature on Spark

One important challenge of dynamic facial emotion recognition is to effectively obtain the spatial and dynamic change of face structure from videos. Besides, there is an increasing demand for distributed computing of videos, as a result of the speedy production of videos from numerous multimedia sources. To address the above issues, in this work, we propose a novel method for dynamic facial emotion recognition on top of Spark. Furthermore, we introduce an effective dynamic feature descriptor namely, Volume Symmetric Local Graph Structure (VSLGS), which extracts the spatiotemporal features. We also utilize the convolutional neural network (CNN) to obtain deep spatial features. Lastly, these obtained features are concatenated and fed to Spark MLlib Multilayer Perceptron (MLP) classifier to recognize the dynamic facial emotions. An extensive experimental investigation is performed to prove the effectiveness of our method over state-of-the-art methods. Furthermore, we also showed the scalability of the proposed method experimentally.