Feature Extraction Using Gabor-Filter and Recursive Fisher Linear Discriminant with Application in Fingerprint Identification

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.

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Integrated Feature Extraction Using Gabor-Filter and Recursive Support Vector Machine for Fingerprint Identification and Verification

International Conference on Software Technology and Engineering, 3rd (ICSTE 2011) ◽

10.1115/1.859797.paper6 ◽

2011 ◽

pp. 31-37

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Gabor Filter ◽

Fingerprint Identification ◽

Support Vector

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.886.515 ◽

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.

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

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001411008750 ◽

2011 ◽

Vol 25 (04) ◽

pp. 491-512 ◽

Cited By ~ 9

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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