A Transfer Learning Approach for Face Recognition Using Average Pooling and MobileNetV2

Abstract

Facial recognition is a fundamental method in facial-related science such as face detection, authentication, monitoring, and a crucial phase in computer vision and pattern recognition. Face recognition technology aids in crime prevention by storing the captured image in a database, which can then be used in various ways, including identifying a person. With just a few faces in the frame, most facial recognition systems function sufficiently when the techniques have been tested under artificial illumination, with accurate facial poses and non-blurry images. In our proposed system, a face recognition system is proposed using average pooling and MobileNetV2. The classifiers are implemented after a set of preprocessing steps on the retrieved image data. To compare the model is more effective, a performance test on the result is performed. It is observed from the study that MobileNetV2 triumphs over average pooling with an accuracy rate of 98.89% and 99.01% on training and test data, respectively.