Palm Print Recognition using Zernike Moments

Karar, Subhajit; Parekh, Ranjan

doi:10.5120/8839-3069

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…The proposed system was also compared with the other recognition systems, particularly face recognition system [ 27 ], fingerprint recognition system [ 28 ], palm print recognition system [ 29 ], and hand shape recognition system [ 22 ] using Zernike Moment and similar databases. The comparative results in Table 3 prove that the average verification accuracies at 0.01% FAR of our system can perform better than other recognition systems in terms of recognition rate.…”

Section: Resultsmentioning

confidence: 99%

Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

Tran

2017

Computational Intelligence and Neuroscience

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In this paper, the authors present a novel personal verification system based on the likelihood ratio test for fusion of match scores from multiple biometric matchers (face, fingerprint, hand shape, and palm print). In the proposed system, multimodal features are extracted by Zernike Moment (ZM). After matching, the match scores from multiple biometric matchers are fused based on the likelihood ratio test. A finite Gaussian mixture model (GMM) is used for estimating the genuine and impostor densities of match scores for personal verification. Our approach is also compared to some different famous approaches such as the support vector machine and the sum rule with min-max. The experimental results have confirmed that the proposed system can achieve excellent identification performance for its higher level in accuracy than different famous approaches and thus can be utilized for more application related to person verification.

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Section: Resultsmentioning

confidence: 99%

Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

Tran

2017

Computational Intelligence and Neuroscience

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“…In latest work in palm print recognition [27,28,29] the reasercher has used direct cosine transform, 2DDFT and local binary pattern for feature ext raction and Euclidean distance, correlat ion value and chi square statistics for recognition.…”

Section: Classification or Recognitionmentioning

confidence: 99%

Palmprint Recognition: A Survey- Ketan Patel

2015

IJAERD

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In this paper we have used palm print recognition system as it is unique as compared to face recognition and rigid as compare to fingerprint recognition system. Different recognition algorithms are consists of four processing parts 1 Preprocessing, 2 Region of interest, 3 Feature Extraction and 4 Classification. We have discussed different standard databases which are used to validate recognition algorithm and also focused on various preprocessing techniques used by many researchers. We have also looked upon various feature extraction techniques like PCA, LDA, ICA, 2 DPCA, 2 DDFT, Wavelet, Gabor and classification techniques like Euclidean Distance, Neural Network, Support Vector Machine. In this paper different combination of above three techniques are reviewed.

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“…A lot of AI-ROI with different wavelet filter families were investigated with competitive indicator and PCA, via using a palm database of Hong Kong Polytechnic University (PolyU) AIROI on daubechies-7 (DB-7) wavelet filter attaining genuine acceptance rate (GAR) of 99.67% and equal error rate (EER) of 0.0152%. A study conducted by Parekh and Karar [7] proposed a system to recognize palmprints for the individual's biometric matching. Complicated Zernike moments (ZM) have been structured with the use of many complicated polynomials forming the whole orthogonal base series and determining unit disc.…”

Section: Introductionmentioning

confidence: 99%

Palm print recognition based on harmony search algorithm

Mustafa

Chyad

Haleot

2021

IJECE

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Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.

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Palm Print Recognition using Zernike Moments

Cited by 7 publications

References 15 publications

Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

Palmprint Recognition: A Survey- Ketan Patel

Palm print recognition based on harmony search algorithm

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