Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks
This work addresses the problem of real-time emotion recognition from static images, which is incremental as it builds on existing facial landmark and neural network methods.
The paper tackles real-time facial expression recognition by developing a lightweight algorithm that extracts geometric and texture-based features from facial landmarks and uses a Multi-Layer Perceptron for classification, achieving 96% accuracy on the test set.
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.