scholarly journals Enhancing Fixed Size Palmprint Region of Interest (ROI) Extraction Algorithm for Personal Identification

2019 ◽  
Vol 8 (4) ◽  
pp. 6918-6923
Keyword(s):  
Region Of Interest ◽  
Recognition System ◽  
Small Region ◽  
Personal Identification ◽  
Fixed Size ◽  
Extraction Area ◽  
Extraction Region ◽  
Extraction Algorithm ◽  
Roi Extraction ◽  
Biometrics Recognition

Identification and verification are the fundamental process in biometrics recognition system. Research indicates that palmprint, as one of the biometric recognitions system is commonly used for human identification. It is because there are many features and information contained inside the palmprint that can be used in the identification process. However, only a small region of the palmprint can be extracted using the existing palmprint region of interest (ROI) extraction algorithms. This has become a problem for identification systems due to negligible and loss of important features which are located outside the ROI. Hence, it is a necessity to improve the palmprint ROI extraction algorithm whereby bigger palmprint ROI can be extracted using this algorithm. Therefore, a larger fixed size extraction algorithm for palmprint ROI is proposed where the extraction region is larger so that more important identification features can be captured inside these ROIs. The performance between proposed and existing extraction algorithms are tested based on two characteristics which are the palmprint ROI extraction area and the comparison of feature creases extracted in a palmprint ROI. The results show that 300x300 fixed size ROI is able to capture 13 out of 14 creases attributes for palmprint identification. This implies that the proposed extraction algorithm shows a promising method of extraction as compared to the existing algorithms.

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%.

scholarly journals Enhanced Region of Interest Extraction method for Finger Vein Images

2021 ◽  
pp. 13-25
Author(s):  
Prof Hindrustum Shaaban
Keyword(s):  
Extraction Method ◽  
Region Of Interest ◽  
Recognition System ◽  
Finger Vein ◽  
Corrected Image ◽  
Effective Manner ◽  
Roi Extraction ◽  
Vein Recognition ◽  
Oblique Images ◽  
Finger Vein Recognition

Extracting Region of Interest (ROI) is an important step for finger vein recognition system. The purpose of this process is to determine the part of the image that we need for extracting features. In this paper we present an ROI extraction method that overcome the problems of finger rotation and displacement. We first locate the finger midline to be used in correcting the oblique images. We then use a sliding window to determine the Proximal inter phalangeal joint and to further identify the ROI height. Finally, from the corrected image of a certain height, the ROI is obtained through the use of finger edges internal tangents as ROI boundaries. The results prove that our method in a more accurate and effective manner in comparison with the method of [1], and thus enhance the performance of the system.

Biometric Authentication Based on Hand Vein Pattern

2013 ◽  
pp. 52-64 ◽  
Author(s):  
Munaga V. N. K. Prasad ◽  
Ilaiah Kavati
Keyword(s):  
Recognition System ◽  
Human Hand ◽  
Geometric Information ◽  
Vein Pattern ◽  
Biometric Technology ◽  
Hand Vein ◽  
Extraction Algorithm ◽  
Roi Extraction ◽  
Key Features ◽  
Mission Critical

Recently, a new biometric technology based on human hand vein patterns has attracted the attention of many researchers. This chapter discusses vein pattern authentication, which uses the vascular patterns of the back of the hand as personal authentication data. Vein information is hard to duplicate because veins are internal to the human body. Vein authentication is one of the most accurate and reliable biometric technologies, which is widely employed in mission-critical applications such as banking, etc. A dynamic ROI extraction algorithm was presented through which more features can be extracted when compared to the fixed ROI. The extracted ROI was enhanced, and then the noise content was removed. The key features that represent the geometric information of the vein pattern were extracted; they are the bifurcation and ending points. This chapter presents a new vein pattern recognition system by assigning different weights to bifurcation and ending points. The approach is tested on a vein pattern database of 60 different hands. Experimental results show the approach achieves 2.5% of Equal Error Rate (EER) and recognition accuracy of 98.24%.

Region of interest extraction for image quality assessment

2020 ◽  
Vol 2020 (9) ◽  
pp. 321-1-321-9
Author(s):  
Runzhe Zhang ◽  
Eric Maggard ◽  
Yousun Bang ◽  
Minki Cho ◽  
Jan Allebach
Keyword(s):  
Digital Image ◽  
Extraction Method ◽  
Image Quality Assessment ◽  
Region Of Interest ◽  
Ground Truth ◽  
Extraction Process ◽  
Print Quality ◽  
Extraction Algorithm ◽  
Color Vector ◽  
Roi Extraction

Print quality (PQ) is most important in the printing industry. To detect and analyze print defects is an effective solution to improve print quality. As the different types of print defects appear in different regions of interest (ROI) in the digital image of a scanned page, extracting the different ROIs helps to detect and analyze the printer defect. This paper proposes a method to extract different ROIs based on the digital image object map [1], which includes three different labels: raster (images or pictures), vector (background and smooth gradient color areas), and symbol (symbols and texts). Our ROI extraction method will extract four kinds of ROIs based on these three labeled objects. So we need to distinguish the background area and smooth gradient color area (color vector) from other vector objects. The process of the ROI extraction method includes four parts; and each part will extract one kind of ROI. For the color vector and background ROI extraction part, we develop two approaches: one is to obtain the maximum area rectangular ROI; and the other approach is to extract the deepest rectangular ROI. With both of these two methods, we use a greedy algorithm to gather additional useful ROIs. In the final result of the ROI extraction process, we only save the left top and right bottom positions for each ROI. In the end, we design a Matlab GUI Tool and label the ROI ground truth manually. We calculate the intersection over union (IoU)) between the ROI extraction result and the ROI manually labeled ground truth to evaluate our ROI extraction algorithm, and check whether it is good enough to crop different ROIs from the image of the scanned page to detect and analyze print defects.

scholarly journals Segmentation of Palmprint into Region of Interest (ROI): A Survey

2005 ◽  
Vol 4 (2) ◽  
pp. 613-619
Author(s):  
Sneha M. Ramteke ◽  
Prof. S. S. Hatkar
Keyword(s):  
Feature Extraction ◽  
Image Acquisition ◽  
Region Of Interest ◽  
Small Region ◽  
Physiological Characteristics ◽  
Palmprint Recognition ◽  
Recognition Process ◽  
Roi Extraction

Palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Palmprint recognition process consists of image acquisition, pre-processing, feature extraction, matching and result. One of the most important stages in these methods is pre-processing which contains some operations such as filtering, Region Of Interest (ROI) extraction, normalization. This paper provides a survey on various different methods to segmentation of palmprint into ROI and extraction of principle lines. ROI segmentation of palmprint is to automatically and reliably segment a small region from the captured palmprintimage.We pay more attention towards more essential stage of palm localization, segmentation and ROI extraction. Finally some conclusion and suggestion is offered.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

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.

ALGORITHMS FOR FINGERPRINT CLASSIFICATION

2021 ◽  
Vol 335 (1) ◽  
pp. 39-44
Author(s):  
D. Lebedev ◽  
A. Abzhalilova
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Recognition System ◽  
Personal Identification ◽  
External Object ◽  
Biometric Identification ◽  
Fingerprint Classification ◽  
Combined Application ◽  
Traditional Approaches

Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.

A FULLY AUTOMATED OFFLINE HANDWRITING RECOGNITION SYSTEM INCORPORATING RULE BASED NEURAL NETWORK VALIDATED SEGMENTATION AND HYBRID NEURAL NETWORK CLASSIFIER

2004 ◽  
Vol 18 (07) ◽  
pp. 1267-1283 ◽  
Author(s):  
MOUMITA GHOSH ◽  
RANADHIR GHOSH ◽  
BRIJESH VERMA
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Solution Method ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Neural Network Classifier ◽  
Hybrid Neural Network ◽  
Rule Based ◽  
Extraction Algorithm ◽  
Offline Handwriting Recognition

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

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