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.
Metamorphic Testing for Edge Real-Time Face Recognition and Intrusion Detection Solution
2022-09-01
615259 byte
Conference paper
Electronic Resource
English
Robust real-time face detection
IEEE | 2001
|Robust Real-Time Face Detection
British Library Conference Proceedings | 2001
|Robust Real-Time Face Detection
British Library Online Contents | 2004
|Testing face recognition systems
British Library Online Contents | 1994
|British Library Online Contents | 2005
|