Face recognition using Eigen-Face implemented on DSP processor
Face recognition is the established research area in 2D biometric recognition system and broadly used in a security system. Face recognition system is a physiological biometric information processing based on the two dimensional face image. This thesis focus to develop an automatic face recognition...
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| Format: | Thesis |
| Language: | English |
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| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59817/1/p.1-24..pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59817/2/Full%20Text.pdf |
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| Summary: | Face recognition is the established research area in 2D biometric recognition system and
broadly used in a security system. Face recognition system is a physiological biometric information processing based on the two dimensional face image. This thesis focus to develop an automatic face recognition using holistic features extracted that use the global features represented by low frequency data from face image. Holistic features are extracted using eigenface method where a linear projection technique such as PCA is
used to capture the important information in the image. Face image has low frequency
information such as shape of mouth, eye, and nose which has high discrimination
power. By using PCA, only several number of eigenvector is preserved which belong to
these features. A low dimensional feature space is classified using distance classifier.
Distance classifier is used to calculate the similarity between two data points in the
feature space based on the distance of two vectors. Euclidean distance is used for
matching process. The propose method is tested using a benchmark ORL dataset that
has 400 images of 40 persons. The best recognition rate is 97.5% when tested using 9
training images and 1 testing image represented with 35 PCA coefficients. Using less
number of PCA coefficients is able for the classifier module to be implemented using
hardware such as DSP processor. Euclidean distance classifier is tested using the
TMS320C6713 digital signal processor (DSP). The computational time is less compared
with the offline simulation using PC based. |
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