Masked face recognition based on facenet and genetic algorithm

Abstract

This study proposes a face recognition method based on FaceNet for face masks. FaceNet converts faces into 128-dimensional feature vectors using a network of deep learning models. The smaller the Euclidean distance between different face images, the closer the person is to the same person; conversely, the larger the Euclidean distance, the more different the person is. Wearing a mask affects some feature vector dimensions of FaceNet conversion, resulting in reduced recognition ability. This study uses a Genetic Algorithm (GA) to select and remove the feature vectors affected by mask wearing. The experimental results show that the validation rate value of 55 features removed by the GA is increased from 0.550 to 0.650 for the original unremoved features.