TPR, PPV and ROC based Performance Measurement and Optimization of Human Face Recognition of IoT Enabled Physical Location Monitoring
Ajitkumar S. Shitole1, Manoj H. Devare2
1Ajitkumar S. Shitole, Research Scholar, Amity University Mumbai, India, Asso. Prof, I²IT, Hinjawadi, India.
2Dr. Manoj H. Devare, HoI, AIIT, Amity University Mumbai, India.
Manuscript received on 12 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 3582-3590 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3186078219/19©BEIESP | DOI: 10.35940/ijrte.B3186.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper describes the construction of Internet of Things (IoT) enabled system which not only captures the sensors data in textual and numeric form but also performs live human face recognition to monitor physical location effectively. The dataset used in order to apply supervised machine learning algorithms is the combination of automatically captured live sensor data along with name of the human face recognized or unknown and additional manually introduced class label. Performance measurement of face recognition is done with the help of Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Logistic Regression (LR). The results show that DT gives the best performance with respect to classifier’s accuracy; True Positive Rate, Positive Predictive Value and area under curve of Receiver Operating Characteristics (ROC) for face recognition prediction whether the recognized face is true or false.
Index Terms: Machine Learning, Physical Location Monitoring, Confusion Matrix, ROC, Decision Tree, Naive Bayes, Logistic Regression, K-Nearest Neighbors.
Scope of the Article: Machine Learning