Real Time Face Recognition Using Convoluted Neural Networks

TL;DR

This paper presents a real-time face recognition system leveraging convolutional neural networks, utilizing CoreML API for face detection and a custom dataset for training to achieve accurate identification.

Contribution

The paper introduces a real-time face recognition approach using CNNs with a dataset created from video frames and implementation via CoreML API.

Findings

Achieved accurate real-time face recognition.

Created a dataset from face videos for training.

Demonstrated effectiveness of CNNs with CoreML in practical applications.

Abstract

Face Recognition is one of the process of identifying people using their face, it has various applications like authentication systems, surveillance systems and law enforcement. Convolutional Neural Networks are proved to be best for facial recognition. Detecting faces using core-ml api and processing the extracted face through a coreML model, which is trained to recognize specific persons. The creation of dataset is done by converting face videos of the persons to be recognized into Hundreds of images of person, which is further used for training and validation of the model to provide accurate real-time results.