Combining Biometric Fractal Pattern and Particle Swarm Optimization‐Based Classifier for Fingerprint Recognition

Lin, Chia‐Hung; Chen, Jian-Liung; Gaing, Zwe-Lee

doi:10.1155/2010/328676

Cited by 15 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation (20) is used to find the highest probability. A threshold of rejection is used to confirm the decision, which can be defined as [25] L * . u judge = 0.95 × 1 30…”

Section: Methodsmentioning

confidence: 99%

Combining fractional‐order edge detection and chaos synchronisation classifier for fingerprint identification

Chen¹,

Huang²,

et al. 2014

IET Image Processing

View full text Add to dashboard Cite

This study proposes the combination of fractional-order edge detection (FOED) and a chaos synchronisation classifier for fingerprint identification. Fingerprints have various morphologies and exhibit singular points, which result in fingerprint individuality. Thumbprint images are captured from subjects using an optical fingerprint reader. The identification procedure consists of three stages: image enhancement, feature extraction and pattern identification. The adjustment of grey-scale values is used to enhance the contrast of the image. In order to overcome the limitations of the integral-order method, FOED is used to improve the clarity of the ridge and valley structures in fingerprint images. Using a reference point, it provides a stable sampling window for fingerprint extraction. Multiple CS-based detectors are used to track the differences as dynamic errors between heterogeneous fingerprints, on a one-to-one basis. The maximum-likelihood method performs a comparison of these different dynamic errors to identify individuals. Using 30 laboratory subjects, the proposed hybrid methods have a faster processing time and provide more accurate fingerprint identification.

show abstract

“…Equation (20) is used to find the highest probability. A threshold of rejection is used to confirm the decision, which can be defined as [25] L * . u judge = 0.95 × 1 30…”

Section: Methodsmentioning

confidence: 99%

Combining fractional‐order edge detection and chaos synchronisation classifier for fingerprint identification

Chen¹,

Huang²,

et al. 2014

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…For 7 categories classification, a threshold of rejection was used to confirm the decision. This can be defined as [25]

\begin{matrix} L^{*} > θ_{judge} = 0.5 \times \frac{1}{7} \sum_{j = normal1}^{normal7} L_{j} (\frac{Φ}{Φ (k)}) . \end{matrix}

…”

Section: Multiple Cs Detectors Constructionmentioning

confidence: 99%

Multiple Chaos Synchronization System for Power Quality Classification in a Power System

Huang

Lin

2014

The Scientific World Journal

Self Cite

View full text Add to dashboard Cite

This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, voltage fluctuation phenomena, and voltage interruptions. Multiple detectors are used to monitor the dynamic errors between the master system and the slave system and are used to construct the feature patterns from time-domain signals. The maximum likelihood method (MLM), as a classifier, performs a comparison of the patterns of the features in the database. The proposed method can adapt itself without the need for adjustment of parameters or iterative computation. For a sample power system, the test results showed accurate discrimination, good robustness, and faster processing time for the detection of PQ disturbances.

show abstract

“…technique, proposed by Kennedy and Eberhart [16], is an evolutionary-type global optimization technique developed due to the inspiration of social activities in flock of birds and school of fish and is widely applied in various engineering problems due to its high computational efficiency [6][7][8][9][10][20][21][22][23]. Compared with other population-based stochastic optimization methods, such as GA and ACO, PSO has comparable or even superior search performance for many hard optimization problems, with faster and more stable convergence rates.…”

Section: Pso Algorithm Particle Swarm Optimization (Pso)mentioning

confidence: 99%

PSO-Based PID Controller Design for a Class of Stable and Unstable Systems

Latha

Rajinikanth

Surekha

2013

ISRN Artificial Intelligence

View full text Add to dashboard Cite

Nonlinear processes are very common in process industries, and designing a stabilizing controller is always preferred to maximize the production rate. In this paper, tuning of PID controller for a class of time delayed stable and unstable process models using Particle Swarm Optimization (PSO) algorithm is discussed. The dimension of the search space is only three (, , and ); hence, a fixed weight is assigned for the inertia parameter. A comparative study is presented between various inertia weights such as 0.5, 0.75, and 1. From the result, it is evident that the proposed method helps to attain better controller settings with reduced iteration number. The efficacy of the proposed scheme has been validated through a comparative study with classical controller tuning methods and heuristic methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Finally, a real-time implementation of the proposed method is carried on a nonlinear spherical tank system. From the simulation and real-time results, it is evident that the PSO algorithm performs well on the stable and unstable process models considered in this work. The PSO tuned controller offers enhanced process characteristics such as better time domain specifications, smooth reference tracking, supply disturbance rejection, and error minimization.

show abstract

Combining Biometric Fractal Pattern and Particle Swarm Optimization‐Based Classifier for Fingerprint Recognition

Cited by 15 publications

References 24 publications

Combining fractional‐order edge detection and chaos synchronisation classifier for fingerprint identification

Combining fractional‐order edge detection and chaos synchronisation classifier for fingerprint identification

Multiple Chaos Synchronization System for Power Quality Classification in a Power System

PSO-Based PID Controller Design for a Class of Stable and Unstable Systems

Contact Info

Product

Resources

About