Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks

• URL: http://arxiv.org/abs/2202.00102v1
• Date: Mon, 31 Jan 2022 21:38:30 GMT
• Title: Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks
• Authors: Mohammad Amin Haghpanah, Ehsan Saeedizade, Mehdi Tale Masouleh, Ahmad Kalhor
• Abstract summary: This paper presents an algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner. A Multi-Layer Perceptron neural network is trained based on the foregoing algorithm. A 3-layer is trained using these feature vectors, leading to 96% accuracy on the test set.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set.

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