Palmprint Recognition Based on Image Segmentation of Region of Interest

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.

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Feature Extraction by Decryption to Target

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.1509 ◽

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.

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Palmprint Recognition Based on Subspace Analysis of Gabor Filter Bank

International Journal of Digital Crime and Forensics ◽

10.4018/jdcf.2010100101 ◽

2010 ◽

Vol 2 (4) ◽

pp. 1-15 ◽

Cited By ~ 3

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%.

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Block Statistic for Palmprint Recognition Based on High Frequency Coefficients under Wavelet Transform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.1287 ◽

2012 ◽

Vol 182-183 ◽

pp. 1287-1291

Author(s):

Yu Qin Liu ◽

Wei Qi Yuan ◽

Jin Yu Guo

Keyword(s):

Wavelet Transform ◽

High Frequency ◽

Nearest Neighbor ◽

Feature Vector ◽

Region Of Interest ◽

Palmprint Recognition ◽

Nearest Neighbor Classifier ◽

Palmprint Identification ◽

The Mean ◽

Neighbor Classifier

Palmprint recognition for identification provides a new scheme for information security. This paper presents a block statistic method based on high frequency coefficients under wavelet transform for palmprint identification. Firstly, the method decomposes region of interest (ROI) of the palmprint with the wavelet transform. Then it blocks the high-frequency sub-image. The mean and the variance for each sub-block are found. All the means and the variances constitute feature vector for the image. At last the nearest neighbor classifier is used to classify the images. The method was tested on the basis of UST palmprint image database. From the experimental results, the method can satisfy the uses without excessive demands for collection images.

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Palmprint Recognition Based on Subspace Analysis of Gabor Filter Bank

Crime Prevention Technologies and Applications for Advancing Criminal Investigation ◽

10.4018/978-1-4666-1758-2.ch014 ◽

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

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v4i2c1.4182 ◽

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.

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Image segmentation histogram thresholding based on wavelet transform domain

Advances in Services Science and Services Information Technology ◽

10.2495/sssit132132 ◽

2014 ◽

Author(s):

Xin Fu ◽

Zhikai Huang

Keyword(s):

Image Segmentation ◽

Wavelet Transform ◽

Transform Domain

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State-of-the-Art: A Systematic Literature Review on Image Segmentation in Latent Fingerprint Forensics

Recent Patents on Computer Science ◽

10.2174/2213275912666190429153952 ◽

2019 ◽

Vol 12 ◽

Cited By ~ 2

Author(s):

Megha Chhabra ◽

Manoj Kumar Shukla ◽

Kiran Kumar Ravulakollu

Keyword(s):

Image Segmentation ◽

Background Noise ◽

State Of The Art ◽

Recognition Rate ◽

Poor Quality ◽

Latent Fingerprint ◽

In The Beginning ◽

The Comparative Study ◽

Finger Skin

: Latent fingerprints are unintentional finger skin impressions left as ridge patterns at crime scenes. A major challenge in latent fingerprint forensics is the poor quality of the lifted image from the crime scene. Forensics investigators are in permanent search of novel outbreaks of the effective technologies to capture and process low quality image. The accuracy of the results depends upon the quality of the image captured in the beginning, metrics used to assess the quality and thereafter level of enhancement required. The low quality of the image collected by low quality scanners, unstructured background noise, poor ridge quality, overlapping structured noise result in detection of false minutiae and hence reduce the recognition rate. Traditionally, Image segmentation and enhancement is partially done manually using help of highly skilled experts. Using automated systems for this work, differently challenging quality of images can be investigated faster. This survey amplifies the comparative study of various segmentation techniques available for latent fingerprint forensics.

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Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning

Applied Sciences ◽

10.3390/app11041965 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1965

Author(s):

Raul-Ronald Galea ◽

Laura Diosan ◽

Anca Andreica ◽

Loredana Popa ◽

Simona Manole ◽

...

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Diagnostic System ◽

Region Of Interest ◽

General Purpose ◽

Medical Image Segmentation ◽

Dice Similarity Coefficient ◽

Learning Methods ◽

Study Results ◽

Whole Heart

Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D, slice-by-slice manner, based on region of interest (ROI) localization, applying an optimized training regime to improve segmentation performance from regions of interest. We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general-purpose model, DeepLabV3+. Furthermore, we show that ensembling of these two fundamentally different architectures brings constant benefits by testing our approach on two different datasets, the publicly available ACDC challenge, and the imATFIB dataset from our in-house conducted clinical study. Results on the imATFIB dataset show that the proposed approach performs well with the provided training volumes, achieving an average Dice Similarity Coefficient of the whole heart of 89.89% on the validation set. Moreover, our algorithm achieved a mean Dice value of 91.87% on the ACDC validation, being comparable to the second best-performing approach on the challenge. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting.

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