A Fingerprint Recognition Algorithm Based on Principal Component Analysis

2006 ◽  
Author(s):  
Wang Yongxu ◽  
Ao Xinyu ◽  
Du Yuanfeng ◽  
Li Yongping
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
Fingerprint Recognition

Character Pattern Recognition Algorithm Based on Principal Component Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5313-5318
Author(s):  
Feng Xian Tang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Character Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
License Plate ◽  
Specific Work ◽  
Advantages And Disadvantages

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.

scholarly journals Algorithm of Face Recognition by Principal Component Analysis

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.

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

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.

scholarly journals A face recognition software framework based on principal component analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254965
Author(s):  
Peng Peng ◽  
Ivens Portugal ◽  
Paulo Alencar ◽  
Donald Cowan
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Iris Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Software Framework ◽  
Software Developers ◽  
Practical Applications

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

Sign in / Sign up

Export Citation Format

Share Document