Improved Competitive Code for Palmprint Recognition Using Simplified Gabor Filter

2009 ◽  
pp. 371-377 ◽  
Author(s):  
Jing Wei ◽  
Wei Jia ◽  
Hong Wang ◽  
Dan-Feng Zhu
Keyword(s):  
Gabor Filter ◽  
Palmprint Recognition

Palmprint Recognition Based on Subspace Analysis of Gabor Filter Bank

2010 ◽  
Vol 2 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Moussadek Laadjel ◽  
Ahmed Bouridane ◽  
Fatih Kurugollu ◽  
WeiQi Yan
Keyword(s):  
Filter Bank ◽  
Gabor Filter ◽  
Region Of Interest ◽  
Extraction Process ◽  
Palmprint Recognition ◽  
Linear Discriminant ◽  
Extraction Algorithm ◽  
Roi Extraction ◽  
Gabor Filter Bank ◽  
A New Technique

This paper introduces a new technique for palmprint recognition based on Fisher Linear Discriminant Analysis (FLDA) and Gabor filter bank. This method involves convolving a palmprint image with a bank of Gabor filters at different scales and rotations for robust palmprint features extraction. Once these features are extracted, FLDA is applied for dimensionality reduction and class separability. Since the palmprint features are derived from the principal lines, wrinkles and texture along the palm area. One should carefully consider this fact when selecting the appropriate palm region for the feature extraction process in order to enhance recognition accuracy. To address this problem, an improved region of interest (ROI) extraction algorithm is introduced. This algorithm allows for an efficient extraction of the whole palm area by ignoring all the undesirable parts, such as the fingers and background. Experiments have shown that the proposed method yields attractive performances as evidenced by an Equal Error Rate (EER) of 0.03%.

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.

scholarly journals Genetic Algorithm Based Gabor CNN For Palmprint Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 4895-4899
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Gabor Filter ◽  
Fitness Function ◽  
Palmprint Recognition ◽  
Central Frequency ◽  
The Past ◽  
Tremendous Progress ◽  
2D Gabor Filter

In the field of biometrics, palmprint recognition has received great interest and made tremendous progress in the past two decades. In palmprint recognition, the important step is to extract the discriminative features from the image and compare it with templates for identification and verification tasks. In this paper, a new genetic-based 2D Gabor filter with the Convolutional Neural Network is presented. The scale and orientation details captured by Gabor filters are optimized based on central frequency, which is determined based on genetic algorithm fitness function. The proposed technique is implemented on four publicly available palmprint datasets- PolyU, CASIA, IITD, and Tongji. Experimental results confirm that the proposed technique achieves better accuracy when compared to Palmnet.

Enhanced Minutiae Extraction for High-Resolution Palmprint Recognition

2017 ◽  
Vol 17 (04) ◽  
pp. 1750020 ◽  
Author(s):  
Lunke Fei ◽  
Shaohua Teng ◽  
Jigang Wu ◽  
Imad Rida
Keyword(s):  
High Resolution ◽  
Gabor Filter ◽  
Wide Distribution ◽  
Palmprint Recognition ◽  
The Public ◽  
Minutiae Extraction ◽  
Alignment System ◽  
Abnormal Points ◽  
Minutiae Matching ◽  
Coarse To Fine

A palmprint generally possesses about 10 times more minutiae features than a fingerprint, which could provide reliable biometric-based personal authentication. However, wide distribution of various creases in a palmprint creates a number of spurious minutiae. Precisely and efficiently, minutiae extraction is one of the most critical and challenging work for high-resolution palmprint recognition. In this paper, we propose a novel minutiae extraction and matching method for high-resolution palmprint images. The main contributions of this work include the following. First, a circle-boundary consistency is proposed to update the local ridge orientation of some abnormal points. Second, a lengthened Gabor filter is designed to better recover the discontinuous ridges corrupted by wide creases. Third, the principal ridge orientation of palmprint image is calculated to establish an angle alignment system, and coarse-to-fine shifting is performed to obtain the optimal coordinate translation parameters. Following these steps, minutiae matching can be efficiently performed. Experiment results conducted on the public high-resolution palmprint database validate the effectiveness of the proposed method.

Texture Features in Palmprint Recognition System

2021 ◽  
Vol 10 (1) ◽  
pp. 41-57
Author(s):  
C. Naveena ◽  
Shreyas Rangappa ◽  
Chethan H. K.
Keyword(s):  
Gabor Filter ◽  
Median Filter ◽  
Texture Features ◽  
Recognition System ◽  
Personal Identification ◽  
Palmprint Recognition ◽  
Sum Rule ◽  
Palm Print

This paper describes the algorithm used for personal identification based on features extracted from the palmprint. The local Gabor XOR (LGXP) features is built using Gabor filter with orientation. Initially, the palm print images are preprocessed using median filter. The algorithm is then modified, where features are extracted with different orientations of the Gabor filter called the multiple orientation LGXP (MOLGXP) features. The PCA feature is extracted and fused with MOLGXP and PCA using sum rule.

Palmprint Recognition Based on Subspace Analysis of Gabor Filter Bank

2012 ◽  
pp. 202-214
Author(s):  
Moussadek Laadjel ◽  
Ahmed Bouridane ◽  
Fatih Kurugollu ◽  
WeiQi Yan
Keyword(s):  
Filter Bank ◽  
Gabor Filter ◽  
Region Of Interest ◽  
Extraction Process ◽  
Palmprint Recognition ◽  
Linear Discriminant ◽  
Roi Extraction ◽  
Efficient Extraction ◽  
Gabor Filter Bank ◽  
A New Technique

This paper introduces a new technique for palmprint recognition based on Fisher Linear Discriminant Analysis (FLDA) and Gabor filter bank. This method involves convolving a palmprint image with a bank of Gabor filters at different scales and rotations for robust palmprint features extraction. Once these features are extracted, FLDA is applied for dimensionality reduction and class separability. Since the palmprint features are derived from the principal lines, wrinkles and texture along the palm area. One should carefully consider this fact when selecting the appropriate palm region for the feature extraction process in order to enhance recognition accuracy. To address this problem, an improved region of interest (ROI) extraction algorithm is introduced. This algorithm allows for an efficient extraction of the whole palm area by ignoring all the undesirable parts, such as the fingers and background. Experiments have shown that the proposed method yields attractive performances as evidenced by an Equal Error Rate (EER) of 0.03%.

scholarly journals Gabor Filter and Texture based Features for Palmprint Recognition

2017 ◽  
Vol 108 ◽  
pp. 2488-2495 ◽  
Author(s):  
Ali Younesi ◽  
Mehdi Chehel Amirani
Keyword(s):  
Gabor Filter ◽  
Palmprint Recognition
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