Palmprint recognition using eigen-palm image implemented on DSP processor
This study focuses on the development of a human identification system using eigenpalm images. Human identification based on biometric technology is extensively used in several applications, such as access control and criminal investigation. The proposed method consists of three main stages. The...
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| 格式: | Thesis |
| 语言: | English |
| 主题: | |
| 在线阅读: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61982/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61982/2/Full%20text.pdf |
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| 总结: | This study focuses on the development of a human identification system using eigenpalm
images. Human identification based on biometric technology is extensively used
in several applications, such as access control and criminal investigation. The proposed
method consists of three main stages. The preprocessing stage computes the palmprint
images to capture important information and produce a better representation of
palmprint image data. The second stage extracts significant features from palmprint
images and reduces the dimension of the palmprint image data by applying the principal
component analysis (PCA) technique. A linear projection method is used in this stage to
reduce redundant features and remove noise from the palmprint image. Furthermore,
this approach increases discrimination power in the feature space. The Euclidean
distance classifier is used in the classification stage, which is the third stage. The
proposed method is tested using a benchmark PolyU dataset. Experimental results show
that the best achieved recognition rate is 97.5% when the palmprint image is resized
with 0.2 resizing scale and represented using 34 PCA coefficients. The raw data
projection and Euclidean distance classifier can be implemented on a digital signal
processor (DSP) board. Implementing the proposed algorithm using the DSP board
achieves better performance in computation time compared with a personal computerbased
system which make the system 47.2% faster. |
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