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.

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.

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.

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 A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zahra Amiri ◽  
Hamid Hassanpour ◽  
Azeddine Beghdadi
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Main Idea ◽  
Region Of Interest ◽  
Gastrointestinal Diseases ◽  
Support Vector ◽  
Abnormality Detection ◽  
Video Frames ◽  
Roi Extraction ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by using the joint normal distribution; then, distinct areas are extracted as an ROI segment by considering a threshold. The main idea is to extract abnormal areas in each frame. Therefore, it can be used to extract various lesions in WCE images. In the feature extraction step, three different types of features (color, texture, and shape) are employed. Finally, the features are classified using the support vector machine. The proposed system was tested on the Kvasir-Capsule dataset. The proposed system can detect multiple lesions from WCE frames with high accuracy.

scholarly journals An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Abdu Gumaei ◽  
Rachid Sammouda ◽  
Abdul Malik S. Al-Salman ◽  
Ahmed Alsanad
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
State Of The Art ◽  
Region Of Interest ◽  
Recognition System ◽  
The State ◽  
Palmprint Recognition ◽  
Novel Approach ◽  
And Performance ◽  
Learning Machine

Multispectral palmprint recognition system (MPRS) is an essential technology for effective human identification and verification tasks. To improve the accuracy and performance of MPRS, a novel approach based on autoencoder (AE) and regularized extreme learning machine (RELM) is proposed in this paper. The proposed approach is intended to make the recognition faster by reducing the number of palmprint features without degrading the accuracy of classifier. To achieve this objective, first, the region of interest (ROI) from palmprint images is extracted by David Zhang’s method. Second, an efficient normalized Gist (NGist) descriptor is used for palmprint feature extraction. Then, the dimensionality of extracted features is reduced using optimized AE. Finally, the reduced features are fed to the RELM for classification. A comprehensive set of experiments are conducted on the benchmark MS-PolyU dataset. The results were significantly high compared to the state-of-the-art approaches, and the robustness and efficiency of the proposed approach are revealed.

Research on the Location and Segmentation Technologies in Palmprint Identification

2011 ◽  
Vol 474-476 ◽  
pp. 782-785
Author(s):  
Shuang Xu ◽  
Ji Dong Suo ◽  
Ji Yin Zhao
Keyword(s):  
Feature Extraction ◽  
Index Finger ◽  
Ring Finger ◽  
Region Of Interest ◽  
Middle Finger ◽  
Palmprint Identification ◽  
Roi Extraction ◽  
Image Position ◽  
Correct Image ◽  
Little Finger

In this paper, a method of palmprint segmentation and location is proposed. The proposed method focuses on region of interest (ROI) extraction of palmprint images which involve transition and rotation. Firstly, binary of palmprint image is used to define the edge of palmprint. Then we separate the fingers and palms and find the two valley points of the index finger and middle finger, ring finger and little finger. Finally, rotate image based on the two valley points and correct image position and create coordinate system according to valley points to determine ROI. This method provides a necessary preprocessing for further feature extraction and matching.The effectiveness of the proposed method is verified using the PolyU palmprint database.

scholarly journals Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

2018 ◽  
Vol 8 (7) ◽  
pp. 1210 ◽  
Author(s):  
Mahdieh Izadpanahkakhk ◽  
Seyyed Razavi ◽  
Mehran Taghipour-Gorjikolaie ◽  
Seyyed Zahiri ◽  
Aurelio Uncini
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Region Of Interest ◽  
Support Vector ◽  
Svm Classifier ◽  
Proposed Model ◽  
Roi Extraction ◽  
Deep Region

Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online.

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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