A Multimodal Emotion Recognition System Using Facial Landmark Analysis

This paper introduces a multimodal emotion recognition system based on two different modalities, i.e., affective speech and facial expression. For affective speech, the common low-level descriptors including prosodic and spectral audio features (i.e., energy, zero crossing rate, MFCC, LPC, PLP and temporal derivatives) are extracted, whereas a novel visual feature extraction method is proposed in the case of facial expression. This method exploits the displacement of specific landmarks across consecutive frames of an utterance for feature extraction. To this end, the time series of temporal variations for each landmark is analyzed individually for extracting primary visual features, and then, the extracted features of all landmarks are concatenated for constructing the final feature vector. The analysis of displacement signal of landmarks is performed by the discrete wavelet transform which is a widely used mathematical transform in signal processing applications. In order to reduce the complexity of derived models and improve the efficiency, a variety of dimensionality-reduction schemes are applied. Furthermore, to exploit the advantages of multimodal emotion recognition systems, the feature-level fusion of the audio and the proposed visual features is examined. Results of experiments conducted on three SAVEE, RML and eNTERFACE05 databases show the efficiency of proposed visual feature extraction method in terms of performance criteria.