Real-Time Deep-Learning-Based System for Facial Recognition

This work presents an open code deep-learningbased system to perform facial recognition. The system is composed of five main steps: face segmentation, facial features detection, face alignment, embedding, and classification. We use using deep learning methods for the fiducial points extraction and embedding. Support Vector Machine (SVM) is used for classification task since it is fast for both training and inference. The system achieves an error rate of 0:12103 for facial features detection, which is pretty close to state of the art algorithms, and 0:05 for face recognition. Besides, it is capable to run in real-time.