Evaluating the masked and unmasked face with LeNet algorithm

Abstract

Face recognition is a biometric technique that has been widely used in many fields. Most of the face recognition applications are used in the security and surveillance system. However, due to current pandemic, wearing facemasks is an obligation for everybody in public places. Hence, face recognition will encounter a problem such as extracting the facial features due to blockage caused by the facemasks. Thus, it will lower down the recognition rate level. The collected dataset consists of two categories which is masked face and unmasked face. This dataset called FaceMask Dataset was obtained from kaggle website. The Multi-Task Cascaded Neural Network (MTCNN) was used to find the face region in the dataset, and it will undergo image feature extraction and remove the undetected face as to prepare a proper training dataset before it can be trained by using LeNet algorithm. As the result, the categories in this work are fall into two classes, which masked face and unmasked face. The training accuracy was 98.21% and it will focus on the features on each result and will justify the difference between the accuracy for both classes. The proposed method was able to achieve 98.21% accuracy with LeNet algorithm as the face image was mainly focused on the face area without taking full size of the image.