The increasing use of computing devices and applications in human daily life triggers the need for natural human-computer interaction. Emotion Recognition using multiple features using a semi-serial fusion method is proposed. The study analyses the impact of the feature combinations in the enhancement of the recognition enhancement. The paper presents the use of the multi-view learning principle to a fusion of different features for one emotion expression-based recognition. The results prove that a planned method is operative. The proposed combination method outperforms the use of one type of features and the concatenated way in recognition accuracy, improvement of execution time, and stability.
Facial emotion recognition is a process based on facial expression to automatically recognize individual emotion expression. Automatic recognition refers to creating computer systems that are able to simulate human natural ability of detection, analysis, and determination of emotion by facial expression. Human natural recognition uses various points of observation to make decision or conclusion on emotion expressed by the present person in front. Facial features efficiently extracted aid in improving the classifier performance and application efficiency. Many feature extraction methods based on shape, texture, and other local features are proposed in the literature, and this chapter will review them. This chapter will survey some recent and formal feature expression methods from video and image products and classify them according to their efficiency and application.
Multimodal emotion recognition has become one of the new research fields of human-machine interaction. This paper focuses on feature extraction and data fusion in audio-visual emotion recognition, aiming at improving recognition effect and saving storage space. A semi-serial fusion symmetric method is proposed to fuse the audio and visual patterns of emotional recognition, and a method of Symmetric S-ELM-LUPI is adopted (Symmetric Sparse Extreme Learning Machine-Learning Using Privileged Information). The method inherits the generalized high speed of the Extreme Learning Machine, and combines this with the acceleration in the recognition process by the Learning Using Privileged Information and the memory saving of the Sparse Extreme Learning Machine. It is a learning method, which improves the traditional learning methods of examples and targets only. It introduces the role of a teacher in providing additional information to enhance the recognition (test) without complicating the learning process. The proposed method is tested on publicly available datasets and yields promising results. This method regards one pattern as the standard information source, while the other pattern as the privileged information source. Each mode can be treated as privileged information for another mode. The results show that this method is appropriate for multi-modal emotion recognition. For hundreds of samples, the execution time is less than one percent seconds. The sparsity of the proposed method has the advantage of storing memory economy. Compared with other machine learning methods, this method is more accurate and stable.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.