A Perancangan Sistem Pengenalan Wajah Secara Real-Time pada CCTV dengan Metode Eigenface: Designing Face Recognition Systems in Real-Time on CCTV with the Eigenface Method

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The development of times and curiosity in a condition become a reason for people to continue to develop security systems at home, one of which is by CCTV. Basically, CCTV security systems only function as recording devices on the scene. Therefore, the security level of the CCTV is still low. For that we need a system that can be a security solution. The system can detect objects in the form of faces as image input. To insert image objects into the system, the system requires a camera. The object detected by the camera will do a matching face with the face image contained in the dataset class. The system is the application of Computer Vision in the security system. Brain memory will provide a picture of a face that we have known before. The analogy can be likened to a machine or device that has the same ability as humans to recognize individuals through facial images. Through this research a comparison of facial image recognition with eigenface algorithm using feature extraction, PCA and LDA will be implemented on a real-time computer platform. The library used in Eigenface is OpenCV. The purpose of this study is to find out which method has a high degree of accuracy in performing facial image recognition by comparing between the two methods used. The problem faced by the author when performing accuracy tests is the different light levels between the dataset and the test subject, and changes in attributes such as hair and beard can affect the resulting accuracy. Based on the test results it is known that the accuracy produced by the Eigenface PCA is better than the LDA eigenface. The best accuracy on eigenface was obtained with a PCA combination of 98.06%.

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How to Cite
JAMHARI, A. (2020). A Perancangan Sistem Pengenalan Wajah Secara Real-Time pada CCTV dengan Metode Eigenface:. Journal of Informatics, Information System, Software Engineering and Applications (INISTA), 2(2), 20-32. https://doi.org/10.20895/inista.v2i2.117


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