Palmprint Recognition using Robust Template Matching

Poonia, Poonam; Ajmera, Pawan K.; Shende, Vijayendra

doi:10.1016/j.procs.2020.03.338

Cited by 17 publications

(19 citation statements)

References 15 publications

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“…Al. in their research, "palm print recognition using robust template matching" yields better results in terms of Correct Recognition Rate (CRR) and Equal Error Rate (EER) of 95.4% and 0.37% respectively [ 18].…”

Section: Literature Reviewmentioning

confidence: 99%

Palm Print Features for Personal Authentication Based On Seven Moments

Ali¹,

Alsaif²

2020

AL-Rafidain Journal of Computer Sciences and Mathematics

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Biometric images are considered as one of the major coefficients in the field of personal authentication. One of the main approaches for personal identification is based on palm print. So studying the features extracted from palm print image adopted to get high efficient system for any recognition systems. In this research two major phases are hold on, in the first phase a database was built for 100 persons by acquiring four images for both hands (4 for left hand and 4 for right hand), then processed to extract ROI (region of interest) by looking for the palm centroid then a square area is fixed based on that centroid. The pre-process play an important step for stable features. Evaluation of the seven moments for each image (8 images) follow the preprocess then stored in the database file (so each person will have 56 values), this phase called personal database preparation. The second phase is the detection phase, which requires the same steps to get 56 values then go through the database looking for the closest person to the tested one. The system evaluation measured by statistical metrics which show good result goes up to 95.7% when applied on 50 persons with different conditions. Also the effect of ROI dimension with individual hands and integrated both of them studied, and the recommended dimension is 192*192.

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Section: Literature Reviewmentioning

confidence: 99%

Palm Print Features for Personal Authentication Based On Seven Moments

Ali¹,

Alsaif²

2020

AL-Rafidain Journal of Computer Sciences and Mathematics

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“…To derive a smooth image, we have applied the filter coefficient to each pixel point and convolute the For all the parameters tested, the minimum Equal Error Rates (EER) of all methods are calculated. ERR is the point where False Acceptance Rate (FAR) and False Rejection Rate (FRR) overlaps which is equal to both rates [31]. For all the parameters tested, the minimum EER of all methods are listed in table 2.…”

Section: Finding Roi Of the Imagementioning

confidence: 99%

A Comparative Analysis of Different Feature Extraction Techniques for Palm-print Images

Pratyasha

Swarnkar

Padhy

2020

Preprint

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In this advanced decade, automatic identification of individuals is a significant achievement due to the high demand of security system. Hence, individual recognition using biometrics data is leading in the field of image processing. Although biometrics data analysis using thumb impression and finger-prints are very popular since many years, sometimes it leads to false acceptance and rejection if any physical change occurs in the finger ridges. There may be a high risk of hacking the biometrics data which is now a big challenge for cyber security employees. This paper captures the palm-print images of individuals as referred biometrics data for individual recognition. The research work is based on one of the prior issue that is feature extraction to extract the features of palm-print image such as principle lines, textures, ridges and pores etc. For this, some of the feature extraction techniques such as Derivatives of Gaussian filter (DoG), Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) and competitive coding. Two types of filters: Gaussian Filter and Gabor filter are combined with each of the feature extraction scheme for the matching of sampled image with testing image. In the result, the error rates of each of the feature extraction algorithms are compared to recognize the palm image of two different individuals.

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“…Utilizing the physical and behavioral characteristics of humans is the main goal of a biometric systems for personality recognition [1]. Physiological features such as iris and fingerprints are unique to each person and are stable and it is not possible to repeat or observed as a person's characteristics in addition to the inability to steal them [2].…”

Section: -Introductionmentioning

confidence: 99%

Personal Authentication Based on Curvelet Transform of Palm Print Moments

Basheer¹,

Alsaif²

2022

Tikrit J. Pure Sci.

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Image transformation provide deep meaning about images feature, so many type of image transformation are appear in the last decade years, one of them is curvelet transformation which improve the image processing techniques specially in field of feature extraction. Personal authentication adopt biometric information to be one of the major coefficients in this field. Palm print one of the main approaches for personal identification. So studying the moments extracted from coefficients of curvelet transform of palm print image adopted in order to get high efficient system for personalization systems. Two major phases are constructed in this research to adopt the moments of low frequency coefficient of the curvelet for personal identification. In the first phase a database was built for 50 persons by acquisition nine images for both hands (9 for left hand and 9for right hand). images are acquired and then processed to extract ROI (region of interest) by looking for the palm centroid then a square shape will be fixed based on that centroid. This preprocess play an important step for stable features. Histogram is applied to the images and then apply SOBLE operator and morphological operation to highlight features of palm print, then apply decomposition on each image based on curvelet transformation. Select low frequency coefficient (which hold the details). Evaluation of seven moments for each image (18images) then store there in the database file (so each person will have 126 values), this phase called personal database preparation. While the second phase is the detection phase, which apply the same steps to evaluate the moments as done in first phase then go through the database looking for the closest person to the tested one. System evaluation measured by statistical metrics which show good result goes up to 96% when applied on 50 person with different acquisition conditions. Also the effect of ROI dimension with individual hands and integrated both of them studied, which yield to recommended dimension of 192*192.

show abstract

Palmprint Recognition using Robust Template Matching

Cited by 17 publications

References 15 publications

Palm Print Features for Personal Authentication Based On Seven Moments

Palm Print Features for Personal Authentication Based On Seven Moments

A Comparative Analysis of Different Feature Extraction Techniques for Palm-print Images

Personal Authentication Based on Curvelet Transform of Palm Print Moments

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