article · Computational Intelligence and Neuroscience
A real-time approach has been developed to detect human emotions from facial expressions for use in robotic vision and interactive communication. The system comprises four distinct phases: preprocessing, key point generation, key point selection with angular encoding, and classification. Facial key points are generated using the deep-learning-based MediaPipe face mesh algorithm, then processed through dedicated mesh generator and angular encoding modules. Principal Component Analysis is applied to decompose the features to boost detection performance. The resulting features are classified using standard machine learning models, including Support Vector Machines, k-Nearest Neighbour, Naive Bayes, Logistic Regression, and Random Forest, as well as a Multilayer Perceptron neural network. Evaluated across different datasets, the methodology attains a human emotion detection accuracy of 97 per cent.
Enabling machines to interpret human facial expressions accurately is critical for natural human-machine interaction. By combining efficient key point tracking with standard classification algorithms, this pipeline provides a high-accuracy method for real-time visual emotion recognition, supporting the development of more responsive interactive and robotic systems.
The primary applications are in robotic vision and interactive robotic communication where real-time responsiveness is essential. Likely users include developers of interactive interfaces and service robotics. Because the performance was validated through simulations across datasets rather than field-tested on physical devices, the technology sits at an applied, tested software stage requiring further embedded integration before practical commercial deployment.
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Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and visual modalities. Facial expressions can be considered as ideal means for detecting the persons' emotions. This paper presents a real-time approach for implementing emotion detection and deploying it in the robotic vision applications. The proposed approach consists of four phases: preprocessing, key point generation, key point selection and angular encoding, and classification. The main idea is to generate key points using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis (PCA). This phase is deployed to enhance the accuracy of emotion detection. Finally, the decomposed features are enrolled into a Machine Learning (ML) technique that depends on a Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), or Random Forest (RF) classifier. Moreover, we deploy a Multilayer Perceptron (MLP) as an efficient deep neural network technique. The presented techniques are evaluated on different datasets with different evaluation metrics. The simulation results reveal that they achieve a superior performance with a human emotion detection accuracy of 97%, which ensures superiority among the efforts in this field.
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DOI: 10.1155/2022/8032673
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