<![CDATA[At present, human intervention is still needed in most security systems to control their functions. The implementation of machine learning plays an important role in smart home security systems for better control functions. The system will have the ability to learn user behaviour, which then represents it in the form of control of the system. One of the important capabilities possessed by a security system is to recognize the face of everyone who accesses a secured place. This paper introduces a face recognition algorithm which is enhanced through a filtration of its controlled Euclidean Distance. The Success Rate Formula is also added and applied for more convincing results. All required system functions are identified and registered as the first system development step. The type of sensor for each function is determined as input data for machine learning processing. Designing and coding the system is carried out on Arduino as its core physical control system before testing and evaluating the system.]]>
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