Palmprint Recognition Algorithm Based on Principal Component Analysis

With the help of the study on mathematical theory and its progress and the development of the computer techniques, digital image processing technology has more and more been applied in each field. The pattern recognition judges unknown things by substituting machine for human eyes, which has a high application value. Thus, it becomes the major branch in image processing fields. The character recognition technology has developed rapidly because of its broad application prospect. Until now, it has been applied successfully in OCR and vehicle license plate recognition. However, it has certain difficulty for the pattern recognition to meet the specific requirements related to specific work scenes. This essay discusses several Eigen value selecting approaches and analyzes the advantages and disadvantages of each. For the template matching methods with penalty factors, in design, character recognition algorithm based on the principal component analysis is realized where scattering matrix between classes is as produced matrix.

Download Full-text

Palmprint Recognition Using Wavelet Decomposition and 2D Principal Component Analysis

2006 International Conference on Communications, Circuits and Systems ◽

10.1109/icccas.2006.284920 ◽

2006 ◽

Cited By ~ 13

Author(s):

Jiwen Lu ◽

Erhu Zhang ◽

Xiaobin Kang ◽

Yanxue Xue ◽

Yajun Chen

Keyword(s):

Principal Component Analysis ◽

Wavelet Decomposition ◽

Principal Component ◽

Component Analysis ◽

Palmprint Recognition

Download Full-text

Algorithm of Face Recognition by Principal Component Analysis

IIUM Engineering Journal ◽

10.31436/iiumej.v3i2.363 ◽

1970 ◽

Vol 3 (2) ◽

Author(s):

Khalid A. S. Al-Khateeb and Jaiz A. Y. Johari

Keyword(s):

Computer Vision ◽

Principal Component Analysis ◽

Face Recognition ◽

Data Base ◽

Principal Component ◽

Component Analysis ◽

Recognition Algorithm ◽

Computer Vision Applications ◽

Simplified Algorithm ◽

Facial Images

A face recognition algorithm based on Principal Component Analysis (PCA) has been developed and tested for computer vision applications. A database of about 400 facial images was used to test the algorithm. Each image is represented by a matrix (112 x 92), The data base is divided into subsets, where each subset represents one of 10 different individuals. A 96% rate of successful detection and a 90% rate of successful recognition were obtained. Several factors had to be standardized to provide a constrained environment in order to reduce error. The analysis is based on a set of eigenvectors that defines an Eigen Face (EF). The method proved to be simple and effective. The simplified algorithm and techniques expedited the process without seriously compromising the accuracy.

Download Full-text

A License Plate Recognition Method Based on Wavelet Transform and Symmetrical Principal Component Analysis Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1743 ◽

2013 ◽

Vol 427-429 ◽

pp. 1743-1746

Author(s):

Xue Feng Deng

Keyword(s):

Principal Component Analysis ◽

Wavelet Transform ◽

Recognition Rate ◽

Principal Component ◽

Component Analysis ◽

Recognition Algorithm ◽

License Plate ◽

License Plate Recognition ◽

Analysis Algorithm ◽

Principal Component Analysis Algorithm

In the past, the license plate recognition algorithm has some shortcomings, such as low recognition rate, slow speed of recognition, inaccurate license plate positioning. This paper proposes a new license plate location algorithm based on wavelet transform and the principal component analysis algorithm is used to feature extraction.The experimental results show that this method can reduce the amount of computation and improve the system recognition rate.

Download Full-text