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Face Recognition in Facial Images with Expression Change Using PCA

Author: ZhaoHongWei
Tutor: RenHuoRong
School: Xi'an University of Electronic Science and Technology
Course: Measuring Technology and Instrument
Keywords: Face Recognition Principal Component Analysis Wavelet Transform Half Face structure Parity component
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 307
Quote: 7
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Abstract


Face recognition to become a very challenging task , it requires a combination of multi-disciplinary, multi-domain knowledge . As the human face is having a high degree of similarity of the non-rigid , when the change of the facial expression of the human face , the face recognition result will be greatly affected . So eliminate expression changes in the recognition process is essential . In this paper, for the issue of the impact of changes in the expression on the face recognition bit plane , starting from the face image preprocessing, feature extraction , feature block method , wavelet transform method and the structure of the half face parity component method to curb the impact of changes in the expression . On this basis , a complete face recognition method - the wavelet half face principal component analysis . Firstly, the face image by wavelet transform to extract the low-frequency component , and then use the half- face structure of the low-frequency component is divided into two parts of the upper and lower half face by a certain percentage , and then find the parity components of each part , and ultimately by the principal component analysis (PCA) to extract the principal components . Test face image with the sample face image in the role of the different weight value to the Euclidean distance calculation, in order to the most similar human face . This method is effective to overcome the impact of changes in the expression on the face recognition played a certain role in the inhibition of expression of the . The experimental data show that this method compared with the basic PCA , the recognition rate improved from 80% to 96.88% .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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