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Face Recognition using the Techniques Base on Principal Component Analysis (PCA)
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Face Recognition using the Techniques Base on Principal Component Analysis (PCA)

several past few years, Face Recognition has become more significant. Although, other identification methods such as Fingerprints or Iris recognition are currently in use, face recognition has its own application.

Principal Component Analysis (PCA) has been variously used in pattern recognition applications, especially in face recognition field. In this seminars, several techniques based on standard PCA, Fisher Linear Discriminant (FLD), Face Specific Subspace (FSS) will be discussed. We will review the developed idea used in the 2D-PCA method and its application on the FSS approach.

For the experimental results, several tests have been performed over on three face image databases: ORL, Yale, and UMIST;

The approaches will be illustrated using a web-based face recognition program. The architecture and techniques used in the development phase (Object Orientation Model / Dot Net Inter-operability) will also be discussed. We conclude by discussing open problems that will encompass our future work.

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