A Combined Rule-Based & Machine Learning Audio-Visual Emotion Recognition Approach

This paper proposes an audio-visual emotion recognition system that uses a mixture of rule-based and machine learning techniques to improve the recognition efficacy in the audio and video paths. The visual path is designed using the Bi-directional Principal Component Analysis (BDPCA) and Least-Square Linear Discriminant Analysis (LSLDA) for dimensionality reduction and discrimination. The extracted visual features are passed into a newly designed Optimized Kernel-Laplacian Radial Basis Function (OKL-RBF) neural classifier. The audio path is designed using a combination of input prosodic features (pitch, log-energy, zero crossing rates and Teager energy operator) and spectral features (Mel-scale frequency cepstral coefficients). The extracted audio features are passed into an audio feature level fusion module that uses a set of rules to determine the most likely emotion contained in the audio signal. An audio visual fusion module fuses outputs from both paths. The performances of the proposed audio path, visual path, and the final system are evaluated on standard databases. Experiment results and comparisons reveal the good performance of the proposed system.