Research on Applied Technology of Palmprint Recognition Based on Fisher Linear Discriminant and Improved PCA Algorithm

2014 ◽  
Vol 886 ◽  
pp. 515-518
Author(s):  
Jing Wen Li
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Critical Conditions ◽  
Palmprint Recognition ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Biometric Technology ◽  
Applied Technology ◽  
Pca Algorithm

The information applied technology of palmprint recognition is a biometric technology, it’s based on the effective information on the palm (such as: palmprint) to identifies people. The palmprint is unique and characteristic, these are the identification of critical conditions. The feature extraction of palmprint image is a prerequisite for recognition, feature extraction algorithm depends on the quality of the recognition rate and efficiency. This paper presents a method of palmprint recognition algorithm based on Fisher linear discriminant analysis and improved PCA algorithm. The experimental results show that, the recognition rate is improved.

A Novel Face Recognition Algorithm

2013 ◽  
Vol 718-720 ◽  
pp. 2055-2061
Author(s):  
Cai Rang Zhaxi ◽  
Yue Guang Li
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Feature Space ◽  
Recognition Algorithm ◽  
Analysis Method ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Discriminant Analysis Method ◽  
Pca Method ◽  
Better Than

This paper firstly analyzes the principle of face recognition algorithm, studies feature selection and distance criterion problem, puts forward the defects of PCA face recognition algorithm and LDA face recognition algorithm. According to the deficiencies and shortcomings of PCA face recognition algorithm and LDA face recognition algorithm, this paper proposes a solution -- PCA+LDA. The method uses the PCA method to reduce the dimensionality of feature space, it uses Fisher linear discriminant analysis method to classification, the realization of face recognition. Experiments show that, this method can not only improve the feature extraction speed, but also the recognition rate is better than single PCA method and LDA method.

Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

2014 ◽  
Vol 608-609 ◽  
pp. 459-467 ◽  
Author(s):  
Xiao Yu Gu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Decision Theory ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Modulation Recognition ◽  
The Core ◽  
Feature Parameters ◽  
Communication Signal ◽  
Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

Feature Extraction by Decryption to Target

2014 ◽  
Vol 1008-1009 ◽  
pp. 1509-1512
Author(s):  
Qing E Wu ◽  
Hong Wang ◽  
Li Fen Ding
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Correct Recognition ◽  
Palmprint Recognition ◽  
Feature Extraction Method ◽  
Energy Feature ◽  
Correct Recognition Rate

To carry out an effective classification and recognition for target, this paper studied the target owned characteristics, discussed a decryption algorithm, gave a feature extraction method based on the decryption process, and extracted the feature of palmprint in region of interest. Moreover, this paper used the wavelet transform to extract the energy feature of target, gave an approach on matching and recognition to improve the correctness and efficiency of existing recognition approaches, and compared it with existing approaches of palmprint recognition by experiments. The experiment results show that the correct recognition rate of the approach in this paper is improved averagely by 2.34% than that of the existing recognition approaches.

Channel Selection and Feature Extraction of ECoG-Based Brain-Computer Interface Using Band Power

2010 ◽  
Vol 44-47 ◽  
pp. 3564-3568 ◽  
Author(s):  
Hai Bin Zhao ◽  
Chong Liu ◽  
Chun Yang Yu ◽  
Hong Wang
Keyword(s):  
Feature Extraction ◽  
Brain Computer Interface ◽  
Channel Selection ◽  
Computer Interface ◽  
Fisher Linear Discriminant ◽  
Data Set ◽  
Linear Discriminant ◽  
Distinctive Features ◽  
The Subject ◽  
Components Analysis

Electrocorticography (ECoG) signals have been proved to be associated with different types of motor imagery and have used in brain-computer interface (BCI) research. This paper studies the channel selection and feature extraction using band powers (BP) for a typical ECoG-based BCI system. The subject images movement of left finger or tongue. Firstly, BP features were used for channel selection, and 11 channels which had distinctive features were selected from 64 channels. Then, the features of ECoG signals were extracted using BP, and the dimension of feature vector was reduced with principal components analysis (PCA). Finally, Fisher linear discriminant analysis (LDA) was used for classification. The results of the experiment showed that this algorithm has got good classification accuracy for the test data set.

Preprocessing and Feature Extraction, Coding, Matching Algorithm for Fingerprint Image

2013 ◽  
Vol 805-806 ◽  
pp. 1900-1906
Author(s):  
He Ping Jia
Keyword(s):  
Feature Extraction ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Development Platform ◽  
Traditional Algorithm ◽  
Fingerprint Image Enhancement ◽  
Global And Local

A set of fingerprint recognition algorithm was achieved mainly including Gamma controller normalization and equalizing, fingerprint image division, fingerprint image binarization and different direction Gabor filter for feature extraction; especially Fingerprint image enhancement and the textures based on Gabor filter, taking account of both global and local features of the fingerprints.using matlab 7.0 for development platform was verified,The experimental results showed the proposed algorithm can avoid all sorts of false characters more effectively and recognition rate is higher than traditional algorithm in the same conditions.

An Algorithm of Frequent Neighboring Class Set Mining with Constraint Class

2011 ◽  
Vol 211-212 ◽  
pp. 808-812
Author(s):  
Gang Fang ◽  
Hong Ying ◽  
Jiang Xiong ◽  
Yuan Bin Wu
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Dimensional Space ◽  
Recognition Rate ◽  
Low Frequency ◽  
Expression Change ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Low Dimensional ◽  
The Impact

In this paper, the purpose is to find a method that can be more suited to facial expression change and also improve the recognition rate. The proposed system contains three parts, wavelet transform, Fisher linear discriminant method feature extraction and face classification. The basic idea of the proposed method is that first extract the low-frequency components through wavelet transform, then the low-frequency images mapped into a low-dimensional space by PCA transform, and finally the utilization of LDA feature extraction method in low-dimensional space. The algorithms were tested on ORL and Yale face database, respectively. Experimental results shows that the proposed method not only improve the recognition rate, but also improve the recognition speed. This method can effectively overcome the impact of expression changes on face recognition, and play a certain role in inhibition of expression.

MEAN AND STANDARD DEVIATION AS FEATURES FOR PALMPRINT RECOGNITION BASED ON GABOR FILTERS

2011 ◽  
Vol 25 (04) ◽  
pp. 491-512 ◽  
Author(s):  
MEIRU MU ◽  
QIUQI RUAN
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Standard Deviation ◽  
Gabor Filter ◽  
Recognition Performance ◽  
Gabor Filters ◽  
Gabor Wavelet ◽  
Palmprint Recognition ◽  
Linear Discriminant ◽  
Gabor Wavelet Transform

The two-dimensional (2D) Gabor function has been recognized as a very useful tool in feature extraction of image, due to its optimal localization properties in both spatial and frequency domain. This paper presents a novel palmprint feature extraction method based on the statistics of decomposition coefficients of the Gabor wavelet transform. It is experimentally found that the magnitude coefficients of the Gabor wavelet transform within each subband uniformly to approximate the Lognormal distribution. Based on this fact, we create the palmprint representation using two simple statistics (mean and standard deviation) as feature components after applying the logarithmic transformation of Gabor filtered magnitude coefficients for each subband with different orientations and scales. The optimum setting of the number of Gabor filters and orientation of each Gabor filter is experimentally determined. For palmprint recognition, the popularly used Fisher Linear Discriminant (FLD) analysis is further applied on the constructed feature vectors to extract discriminative features and reduce dimensionality. All experiments are both executed over the CCD-based HongKong PolyU Palmprint Database of 7752 images and the scanner-based BJTU_PalmprintDB (V1.0) of 3460 images. The results demonstrate the effectiveness of the proposed palmprint representation in achieving the improved recognition performance.

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