Masked Face Recognition and Identification Using Convolutional Neural Networks

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Poonpinij, Pavat; Tarnpradab, Sansiri; Lumpoon, Nattawut Na; Wattanapongsakorn, Naruemon

ผู้เผยแพร่: Institute of Electrical and Electronics Engineers Inc.

ปีที่เผยแพร่ (ค.ศ.): 2023

หน้าแรก: 571

หน้าสุดท้าย: 576

จำนวนหน้า: 6

ISBN: 979-835030686-6

นอก: 2407439X

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178076534&doi=10.1109%2fEECSI59885.2023.10295922&partnerID=40&md5=0f3d7e5996c01f71bb1df4cf3f95935e

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

Since the beginning of the COVID-19 pandemic, everyone has been instructed to wear a face mask that blocks the lower part of their facial area. Only a few existing face recognition models are able to detect and recognize masked faces yet, the average accuracy has worsened when compared to the ones that detect an entire face of a person. Therefore, the objective of this study is to develop new models capable of detecting and recognizing masked faces, and compare their performance with existing models. Three convolutional neural networks (CNNs) are used, namely VGG16, VGGFace, and InceptionResNetV2 (IRv2). Many experimental cases are considered including the cases where only masked-face images are used, or the other two cases where both full-face and masked-face images are used together. The models give maximum accuracy at 93.3% where 50 individuals are recognized and identified. ฉ 2023 IEEE.

คำสำคัญ

masked face recognition