| Summary: | Face recognition has become an increasingly important application in various fields such as security, surveillance, and human-computer interaction. With the rise of facial recognition technologies, there is a growing demand for accurate and efficient systems to recognize faces in real-time.
Eigenface algorithm has been widely used in face recognition systems due to its simplicity, speed, and high recognition rate. This report describes the implementation and evaluation of a real-time face recognition system based on the Eigenface algorithm in MATLAB. The system utilized machine learning techniques to improve its performance and accuracy. Specifically, K-Means, GMM, and DBSCAN were implemented to find an optimal threshold for face classification. A comparison study based on the clustering techniques was conducted to evaluate the effectiveness of the system.
This report provides a detailed discussion of the working principle of the algorithms and the implementation process of the face recognition system. The system is designed to provide real- time recognition of faces in different environments, which can be used in various applications such as security and access control systems.
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