The real-time Face Recognition (FR) techniques are limited to situations where only one face is visible in close proximity to the camera. Sometimes, the people under surveillance may not be directly facing the camera while walking. However, there is a growing need for FR technology to accurately identify multiple faces in crowded areas even when individuals are not facing the camera. An AI-based multiple-face recognition (MFR) system has been developed to improve performance by incorporating an increased number of pose variation samples. The developed system utilizes a pre-trained FaceNet architecture, which converts the face images into a compact Euclidean space of dimension 128 × 1. This study focuses on improving accuracy and decreasing computation time for multiple faces within the field of view. Results show that the FaceNet model has a high recognition rate of 99.7%. The system can recognize up to 10 faces in the field of view at a computational time of 1.21 s.