Improved Two-Phase Framework for Facial Emotion Recognition

Abstract

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated twophase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components -multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

Adaptive Speech Streaming Based on Packet Loss Prediction Using Support Vector Machine for Software-Based Multipoint Control Unit over IP Networks

“…The proposed PLP-based ASSM starts by predicting the loss of a packet to be transmitted in order to determine an appropriate RST mode. To this end, a support vector machine (SVM), which is a popular supervised learning model that finds an optimal hyperplane to analyze the data used for classification and regression [22]- [24], is adopted. Note that the SVM is trained using a radial basis function (RBF).…”