Development of Android Application to identify a person's face
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Abstract
The purposes of this research were to develop an Android application that can identify a person's face by using an Application Programming Interface (API), to evaluate the efficiency of the application performance in identifying faces by comparing the facial feature with a database of faces that have been stored and to evaluate the percentage of application’s accuracy if they were matching. This research was developed in accordance with the SDLC [7] theory. The target group used to assess the satisfaction of the Android application to identify the face of a person included 10 expertise professors in computers and software development. The research instruments were Android application to identify a person's face and a user satisfaction evaluation form. Mean, percentage, and standard deviation were used to analyze the similarity of faces of two groups; the owner face and the similar face.
The results showed that the face identification app could be used according to the purpose of identifying 10 faces from the database had the accuracy of 85% which concluded that the two faces are similar or they are the same person. From the further study, using 10 similar faces to the prototype face had an accuracy of 65%. From comparing the obtained percentages from both experimental groups, the decision criteria were as follow; with an accuracy of more than 80%, it was considered it the same person and accuracy of lower than 70%, it was a different person.
The result showed that the satisfaction of the target on Android application was at the highest level. ( = 4.73, S.D. = 0.45)
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References
[2] L. A. Elrefaei, A. Alharthi, H. Alamoudi, S. Almutairi and F. Al-rammah,( 2017) "Real-time face detection and tracking on mobile phones for criminal detection," 2017 2nd International Conference on Anti-Cyber Crimes (ICACC), Abha, pp. 75-80, doi: 10.1109/Anti-Cybercrime.2017.7905267.
[3] M. Chillaron, L. Dunai, G. P. Fajarnes and I. L. Lengua, (2015)"Face detection and recognition application for Android," IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, pp. 003132-003136, doi: 10.1109/IECON.2015.7392581.
[4] M. Mehra, V. Sahai, P. Chowdhury and E. Dsouza,(2019). Home Security System using IOT and AWS Cloud Services, "International Conference on Advances in Computing, Communication and Control (ICAC3), Mumbai, India, 2019, pp. 1-6, doi: 10.1109/ICAC347590.2019.9089839.
[5] N. Pradeesh, V. Sreejesh Kumar, A. Anand, V. Geetha Lekshmy, S. Krishnamoorthy and K. Bijlani, (2019). "Cost effective and reliable mobile solution for face recognition and authentication," 2019 9th International Conference on Advances in Computing and Communication (ICACC), Kochi, India, pp. 66-69, doi: 10.1109/ICACC48162.2019.8986206.
[6] Stair (1996: 411-412): System development life cycle: SDLC.
