Metamorphic Testing for Edge Real-Time Face Recognition and Intrusion Detection Solution

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...

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Bibliographic Details
Published inIEEE Vehicular Technology Conference pp. 1 - 5
Main Authors Raif, Mourad, Ouafiq, El Mehdi, Rharras, Abdessamad El, Chehri, Abdellah, Saadane, Rachid
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2022
Subjects
Edge Computing
Face recognition
Facial Recognition
Intrusion detection
IoT
Machine learning
Machine learning algorithms
Quality assurance
Smart cities
Smart City
Transfer Learning
Trusted Machine Learning
Vehicular and wireless technologies
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Summary: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.
ISSN:2577-2465
DOI:10.1109/VTC2022-Fall57202.2022.10012836