A SVM-based method for the estimation of fingerprint and palmprint orientation

Wu, Yanping; Zhao, Tong; Wang, Shuguang; Yong, A.; Guo, Tong

doi:10.1109/icicip.2013.6568095

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SVM method has been applied to different types of estimations in different systems [5]. For example the estimations of the road friction constants [23], the fingerprint and palm print orientation [53], the depth of shallow buried objects [41], and alumina powder flow [28] have been conducted using this method. The specific project parameters [45], density with high accuracy [20], and density function [57] can be determined using the SVM method.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Özdemir¹,

Demirtaş²,

Aydın³

2016

NNW

View full text Add to dashboard Cite

Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

show abstract

Section: Introductionmentioning

confidence: 99%

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Özdemir¹,

Demirtaş²,

Aydın³

2016

NNW

View full text Add to dashboard Cite

show abstract

“…Some of them are discussed in this section: Shao and Han et al (2012) [5] use the nonnegative matrix factorization (NMF) to initialize the fingerprint orientation field instead of the gradient-based approach, which obtains more robust results Feng and Jain et al (2013) [6] propose a method based on dictionary learning for estimating the orientation field of latent fingerprints. Also we have proposed some methods for orientation field estimation, for example, Wu and Guo et al (2013) [7] propose a SVMbased method for fingerprint and palmprint orientation field estimation. AnushSankaran et al,2013 [8] defined as Clarity of a latent impression is defined as the discernibility of fingerprint features while quality was defined as the amount of features causal towards matching.…”

Section: Figure-1 Convolution Based Filtration Of Latent Fingerprintsmentioning

confidence: 99%