| Summary: | 碩士 === 國立臺灣師範大學 === 科技應用與人力資源發展學系 === 101 === Face Recognition is one of biometric recognitions used to identify a person by facial features. Principle Component Analysis（PCA) has been widely used in face recognition because it can retain the main component of facial features, and therefore reduce the data dimension and computing complexity. Using PCA for face recognition has two phases：training and recognition. In the training phase, the traditional PCA calculates the covariance matrix of training samples and obtains the principal component eigenvectors. In the recognition phase, testing images are first projected to the principal component eigenvectors and then the recognition results are determined by calculating the projection distance of the testing images with the training samples.
For high-dimensional data, the computing time and required memory for PCA to calculate eigenvectors of the covariance matrix is quite considerable. In this thesis, we propose to accelerate PCA using graphical processing units (GPU) both in training and recognition phases. In the training phase, NIPALS and GS algorithms are accelerated while in the recognition phase, the projection to the eigenvectors of training and testing samples are accelerated.
Experimental results show that the proposed NIPALS GPU approach achieves 5.9 times faster and GS GPU approach achieves 5.25 times faster in the training phase and 1.57 times faster in the recognition phase than the Eigenface implementation of OpenCV.
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