Finger Knuckle Identification using Principal Component Analysis and Nearest Mean Classifier

Neware, Shubhangi; Mehta, Kush P.; Zadgaonkar, A. S.

doi:10.5120/11990-7868

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…Covariance or correlation matrix derived from the data matrix plays an important role in PCA to calculate its eigenvalues and eigenvectors to obtain the associated components that account for most of the variations in the data [11]. For the purpose of this study, correlation matrix is used.…”

Section: Methodology 31 Principal Component Analysis Based Pearson Correlationmentioning

confidence: 99%

Identification of Rainfall Patterns on Hydrological Simulation Using Robust Principal Component Analysis

Shaharudin

Ahmad

Zainuddin

et al. 2018

IJEECS

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A robust dimension reduction method in Principal Component Analysis (PCA) was used to rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of rainfall data. A robust measure in PCA using Tukey’s biweight correlation to downweigh observations was introduced and the optimum breakdown point to extract the number of components in PCA using this approach is proposed. A set of simulated data matrix that mimicked the real data set was used to determine an appropriate breakdown point for robust PCA and compare the performance of the both approaches. The simulated data indicated a breakdown point of 70% cumulative percentage of variance gave a good balance in extracting the number of components .The results showed a more significant and substantial improvement with the robust PCA than the PCA based Pearson correlation in terms of the average number of clusters obtained and its cluster quality.

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Section: Methodology 31 Principal Component Analysis Based Pearson Correlationmentioning

confidence: 99%

Identification of Rainfall Patterns on Hydrological Simulation Using Robust Principal Component Analysis

Shaharudin

Ahmad

Zainuddin

et al. 2018

IJEECS

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“…Breakdown point refers to the smallest contamination fraction that could yield inaccurate outcome [16]. Upon comparing several breakdown points (0.0, 0.2, 0.4, & 0.5) with Tukey's biweight, breakdown point 0.4 emerged as the best as it resulted in more efficient and accurate yields [18].…”

Section: Pca Based Tukey's Biweight Correlationmentioning

confidence: 99%

Spatial Torrential Rainfall Modelling in Pattern Analysis Based on Robust PCA Approach

Shaharudin¹,

Nor²,

Tan³

et al. 2021

Pol. J. Environ. Stud.

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In this research work, the pattern of spatial cluster had been identified for torrential rainfall data within the context of Peninsular Malaysia, which experiences heavy pour annually. Hence, a robust Principal Component Analysis (PCA) technique was employed in this study in order to address problem related to non-balance cluster(s) across patterns of rainfall stemming from skewed rainfall data. To analyze the observations made, Tukey's biweight correlation was applied. For PCA components extraction, the optimum breakdown point was determined based on the proposed method. In order to strike a balance for extraction of number of components, as well as to hinder insignificant spatial scale or low-frequency variation, the simulation data recorded a breakdown point at 70% cumulative percentage of variance. The study outcomes revealed that the robust PCA gave better enhancement than the Pearson-based PCA did for cluster average number and quality. The findings indicate that ten rainfall patterns obtained are quite definite and clearly display the dominant role extended by the complex topography and exchange monsoons of the peninsular.

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“…The texture design delivered by the finger knuckle bowing is singular and makes the surface a particular biometric identifier. Benefits of utilising Finger Knuckle Print (FKP) incorporate rich in textural attributes, effortlessly accessible, contactless image acquisition, invariant to feelings and other behavioural characteristics, for example tiredness, stable features and acknowledgement by the general public (Neware et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Feature level fusion using physical biometric traits

Carol

Fred²

2018

IJBET

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Biometric is a measuring technique that is utilised to discern a person's identity through a physiological or behavioural feature. Each modality on its own cannot always be reliable for recognition. The multimodal biometric system offers several advantages over the traditional biometric system. In order to overcome the limitations of the unimodal biometrics, the fusion of multimodal biometric traits have been used. In this paper, a multimodal biometric based on fingerprint, palm print and knuckle print is presented. Gabor filter is employed to extract features from these biometric traits. The features extracted from these biometric traits are normalised and fused together. The features are fused by feature level fusion, and the result of the proposed method provides efficient authentication, by minimising the FAR by 1.43%, and the framework achieves satisfactory performance.

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Finger Knuckle Identification using Principal Component Analysis and Nearest Mean Classifier

Cited by 14 publications

References 16 publications

Identification of Rainfall Patterns on Hydrological Simulation Using Robust Principal Component Analysis

Identification of Rainfall Patterns on Hydrological Simulation Using Robust Principal Component Analysis

Spatial Torrential Rainfall Modelling in Pattern Analysis Based on Robust PCA Approach

Feature level fusion using physical biometric traits

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