| Title |
Metamorphic Testing for Edge Real-Time Face Recognition and Intrusion Detection Solution |
| ID_Doc |
41259 |
| Authors |
Raif, M; Ouafiq, E; El Rharras, A; Chehri, A; Saadane, R |
| Title |
Metamorphic Testing for Edge Real-Time Face Recognition and Intrusion Detection Solution |
| Year |
2022 |
| Published |
|
| DOI |
10.1109/VTC2022-Fall57202.2022.10012836 |
| Abstract |
Smart city applications are using extensively artificial intelligence for decision-making. Among the fields of application are facial recognition and intrusion detection. The subject is old, but processing techniques and hardware are constantly evolving. This paper will review the most widely known practices and apply them to a smart parking and intrusion detection system using the "JetsonNano" board. Nowadays, quality assurance for machine learning systems is becoming increasingly important. This article focuses on detecting bugs in implementing two classical face recognition algorithms: Eigenface (EF) and Local binary pattern histogram (LBPH). We tested the efficiency of our system using metamorphic testing depending on many factors: weather conditions, pixel noise, and distortion. |
| Author Keywords |
Smart City; Edge Computing; Machine Learning; Facial Recognition; IoT; Trusted Machine Learning; Transfer |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000927580600142 |
| WoS Category |
Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology |
| Research Area |
Engineering; Telecommunications; Transportation |
| PDF |
|