Synthetic Occluded Masked Face Recognition using Convolutional Neural Networks
- Resource Type
- Conference
- Authors
- Recto, Ian James H.; Devaraj, Madhavi
- Source
- 2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 2022 IEEE International Conference on. :124-129 Jul, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Training
Support vector machines
Image recognition
Pandemics
Face recognition
Employment
Communications technology
Face Recognition
Convolutional Neural Network
Partial Face Occlusion
Face Image
Synthetic Image Dataset
Image Processing
- Language
Wearing a face mask is the norm during the COVID–19 pandemic and is advised for enclosed spaces such as workplaces. In face recognition, a face mask is considered a partial occlusion which degrades recognition accuracy. This study focuses on the occlusion factor by a variety of face mask designs. This study aims to mitigate the impact of face masks as an occlusion on a face recognition system. We superimposed a synthetic face mask and black occlusions on top of the face images (FI). FaceNet, a deep convolutional neural network, was used to extract facial embeddings. The faces were classified using a support vector machine. We experimented with different scenarios by using different training sets and testing sets, contains differing mask designs. It achieved a performance of recognizing occluded lower FI with an average accuracy rate of 98.93% in a controlled environment.