scholarly journals Palmprint Recognition Using Hessian Matrix and Two-Component Partition Method

2021 ◽  
Vol 13 (1) ◽  
pp. 26-47
Author(s):  
Jyotismita Chaki ◽  
Nilanjan Dey
Keyword(s):  
Hessian Matrix ◽  
Palmprint Recognition ◽  
Principal Line ◽  
Line Extraction ◽  
New Principal ◽  
Area Of Interest ◽  
Roi Extraction ◽  
Matching Strategy ◽  
Partition Method ◽  
Texture Matching

Palmprint recognition has been comprehensively examined in the past couple of years and various undertakings are done to use it as a biometric methodology for various applications. The point of this study is to construct an effective palmprint recognition technique with low computational multifaceted nature and along these lines to expand the acknowledgment and precision. Since edges are free from distortion, they are very reliable and subsequently used for palm print recognition. The originality of the proposed technique depends on new area of interest (ROI) extraction took after by new principal line extraction and texture matching strategy. The new principal line extraction technique is created by using the Hessian matrix and Eigen value. The texture matching of the ROI is done using new 2-component partition method by segmenting the image into comparative and non-comparative edges. Examinations are finished on a database and exploratory results exhibit that the accuracy of the proposed method is comparable to past methods used for palmprint recognition.

A Palmprint Recognition Approach Based on Image Segmentation of Region of Interest

2016 ◽  
Vol 30 (02) ◽  
pp. 1656002 ◽  
Author(s):  
Qing E Wu ◽  
Zhiwu Chen ◽  
Ruijie Han ◽  
Cunxiang Yang ◽  
Yuhao Du ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Palmprint Recognition ◽  
Characteristic Points ◽  
Positioning Method ◽  
Roi Extraction ◽  
Energy Feature

To carry out an effective recognition for palmprint, this paper presents an algorithm of image segmentation of region of interest (ROI), extracts the ROI of a palmprint image and studies the composing features of palmprint. This paper constructs a coordinate by making use of characteristic points in the palm geometric contour, improves the algorithm of ROI extraction and provides a positioning method of ROI. Moreover, this paper uses the wavelet transform to divide up ROI, extracts the energy feature of wavelet, gives an approach of matching and recognition to improve the correctness and efficiency of existing main recognition approaches, and compares it with existing main approaches of palmprint recognition by experiments. The experiment results show that the approach in this paper has the better recognition effect, the faster matching speed, and the higher recognition rate which is improved averagely by 2.69% than those of the main recognition approaches.

Palmprint Recognition Based on Image Segmentation of Region of Interest

2017 ◽  
pp. 73-90
Author(s):  
QingE Wu ◽  
Weidong Yang
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Palmprint Recognition ◽  
Characteristic Points ◽  
Positioning Method ◽  
Roi Extraction ◽  
Energy Feature

To carry out an effective recognition for palmprint, this paper presents an algorithm of image segmentation of region of interest (ROI), extracts the ROI of a palmprint image and studies the composing features of palmprint. This paper constructs coordinates by making use of characteristic points in the palm geometric contour, improves the algorithm of ROI extraction, and provides a positioning method of ROI. Moreover, this paper uses the wavelet transform to divide up ROI, extracts the energy feature of wavelet, gives an approach of matching and recognition to improve the correctness and efficiency of existing main recognition approaches, and compares it with existing main approaches of palmprint recognition by experiments. The experiment results show that the approach in this paper has the better recognition effect, the faster matching speed, and the higher recognition rate which is improved averagely by 2.69% than those of the main recognition approaches.

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%.

Low-rank representation integrated with principal line distance for contactless palmprint recognition

2016 ◽  
Vol 218 ◽  
pp. 264-275 ◽  
Author(s):  
Lunke Fei ◽  
Yong Xu ◽  
Bob Zhang ◽  
Xiaozhao Fang ◽  
Jie Wen
Keyword(s):  
Low Rank ◽  
Palmprint Recognition ◽  
Principal Line ◽  
Low Rank Representation

scholarly journals Coronary Artery Center-Line Extraction Using Second Order Local Features

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Farsad Zamani Boroujeni ◽  
Rahmita Wirza O. K. Rahmat ◽  
Norwati Mustapha ◽  
Lilly Suriani Affendey ◽  
Oteh Maskon
Keyword(s):  
Hessian Matrix ◽  
Local Features ◽  
Center Line ◽  
Arterial Tree ◽  
Line Extraction ◽  
Look Ahead ◽  
Arterial Lesions ◽  
Feature Based ◽  
And Performance ◽  
Difficult Situations

Of interest is the accurate and robust delineation of vessel center-lines for complete arterial tree structure in coronary angiograms which is an imperative step towards 3D reconstruction of coronary tree and feature-based registration of multiple view angiograms. Most existing center-line tracking methods encounter limitations in coping with abrupt variations in local artery direction and sudden changes of lumen diameter that occur in the vicinity of arterial lesions. This paper presents an improved center-line tracing algorithm for automatic extraction of coronary arterial tree based on robust local features. The algorithm employs an improved scanning schema based on eigenvalues of Hessian matrix for reliable identification of true vessel points as well as an adaptive look-ahead distance schema for calculating the magnitude of scanning profile. In addition to a huge variety of clinical examples, a well-established vessel simulation tool was used to create several synthetic angiograms for objective comparison and performance evaluation. The experimental results on the accuracy and robustness of the proposed algorithm and its counterparts under difficult situations such as poor image quality and complicated vessel geometry are presented.

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%.

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