Assessment of geometric features for individual identification and verification in biometric hand systems

Luque‐Baena, Rafael Marcos; Elizondo, David; López‐Rubio, Ezequiel; Palomo, Esteban J.; Watson, Tim

doi:10.1016/j.eswa.2012.12.065

Cited by 48 publications

(24 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The best EER reported in other approaches is 0.55% for 2D Palm-print + 2D hand geometry + Finger texture (Kanhangad et al, 2011a), whereas our best EER is 0.31% over our JUET dataset. Using GA-LDA, 4.51% EER is reported in (Luque-Baena et al, 2013) and using 80 features of fingers 2.3% of EER are reported in (De-Santos-Sierra et al, 2011) over IITD dataset whereas proposed method is able to gain 0.52% EER over same dataset using hand shape and geometry features (i.e. total 121 features).…”

Section: Comparison With Existing Approachesmentioning

confidence: 61%

“…In contrast to this method, we have utilized the advantages of the shape of hands also which have proved to be a crucial feature along with the geometrical features. Luque-Baena, Elizondo, López-Rubio, Palomo, and Watson (2013) have analyzed the reliability of geometric features of hand and extracted total 403 features. They used combination of Genetic algorithm (GA) and Local discriminative analysis (LDA) and finally produced 35 features.…”

Section: Motivation and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Identity verification using shape and geometry of human hands

Sharma

Dubey

Singh

et al. 2015

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Comparison With Existing Approachesmentioning

confidence: 61%

Section: Motivation and Related Workmentioning

confidence: 99%

Identity verification using shape and geometry of human hands

Sharma

Dubey

Singh

et al. 2015

Expert Systems with Applications

View full text Add to dashboard Cite

“…The advantage of this method is the relatively higher efficiency, and the drawback is the lower accuracy. Arroyo and Fernandez [13] improved the meanvariance assessment method, and proposed the K-means fuzzy assessment method that improved the accuracy of mean-variance assessment method; Luque-Baena et al [14] also proposed the fuzzy assessment method based on K-means and used it to evaluate the robot behavior. Support-confidence method is the one assessing system behavior by the concept of support and confidence, and this method is usually combined with the research of data mining [7,8,[15][16][17].…”

Section: Introductionmentioning

confidence: 98%

Dynamic behavioral assessment model based on Hebb learning rule

Yin

Yuan

Zhang

2016

Neural Comput & Applic

View full text Add to dashboard Cite

Behavioral assessment based on computing system is with important value for computer-simulated training and system diagnosis. However, the existing assessment is a static method for ex post evaluation, and the low efficiency and high complexity have been the urgent problems to be solved in the academic field. In this paper, we propose an adaptive dynamic behavioral assessment model based on Hebb learning rule that effectively combines the assessment standard and the weights of factors. The dynamic behavioral assessment considers the relative weights between the assessment indexes, whereas the existing assessment method does not; the dynamic behavioral assessment uses the assessment standard data recursively and can conduct an instant assessment for the objectives. We have built an assessment system for computer-simulated training, and took the pilot behavioral assessment for example to test and verify the dynamic behavioral assessment mode. Experimental results show that the dynamic behavioral assessment model based on Hebb learning rule has more advantage in assessment efficiency and online computing support.

show abstract

“…For example, Guo et al [24] have proposed a contact free identification system based on hand geometry using 34 hand geometrical features and have reached 96.23% of correct identification rate. Whereas, Luque-Baena et al [13] have utilized initially 403 geometrical measurements of the hand in their contactless hand verification system and selected later pertinent features using Genetic Algorithms (GA) feature selection method. This system reported an equal error rate of 4.51 %.…”

Section: Introductionmentioning

confidence: 99%

Hand verification system based on multi-features fusion

Charfi

Trichili

Alimi

et al. 2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

View full text Add to dashboard Cite

Human hand is a physiological biometric trait employed in order to characterize and identify a person. It is considered as one of the most popular biometric technologies especially in forensic applications, due to its high users acceptance compared to other biometric technologies. In this paper, we pro pose a hand biometric system for personal identity verification, fusing multiple features of the hand at matching score level. In fact, shape and texture are extracted from hand, fingers and palmprint in order to represent the hand image of each person. In the feature extraction strategy, the scale Invariant Feature Transform (SIFT) is extracted from the hand image to describe local invariant features of the hands contour and also extracted from fingers images. In the other hand, Gabor filters are extracted from palmprint images to describe the texture of the hand. The main advantage of these two descriptors (SIFT and Gabor) is that features extracted are invariant to rotation, translation, scale and lighting changes. Personal verification was performed by fusing similarity scores achieved from the hand shape, the fingers and the palmprint. Experimental results show good performances (EER=1.95 %) in hand verification using a database containing 230 different subjects.

show abstract

Assessment of geometric features for individual identification and verification in biometric hand systems

Cited by 48 publications

References 38 publications

Identity verification using shape and geometry of human hands

Identity verification using shape and geometry of human hands

Dynamic behavioral assessment model based on Hebb learning rule

Hand verification system based on multi-features fusion

Contact Info

Product

Resources

About