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Now Vol.5 , Issue.9 September 2017 edition has been published. Authors are cordially invited to submit papers for the upcoming edition through online submission system

New Iris Tracking Method using a Generalized Particle Filter
New Iris Tracking Method using a Generalized Particle Filter
N.Yaghoobi Ershadi1
1 Universidad Politécnica de Madrid, E.T.S. Ingenieros de Telecomunicación, Madrid, Spain.
Correspondence should be addressed to: n.yaghoobi@alumnos.upm.es.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-9 , Page no. 7-15, Sep-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i9.715

Online published on Sep 30, 2017

Copyright © N.Yaghoobi Ershadi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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Citation

IEEE Style Citation: N.Yaghoobi Ershadi, “New Iris Tracking Method using a Generalized Particle Filter”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.7-15, 2017.

MLA Style Citation: N.Yaghoobi Ershadi "New Iris Tracking Method using a Generalized Particle Filter." International Journal of Computer Sciences and Engineering 5.9 (2017): 7-15.

APA Style Citation: N.Yaghoobi Ershadi, (2017). New Iris Tracking Method using a Generalized Particle Filter. International Journal of Computer Sciences and Engineering, 5(9), 7-15.
           
Abstract :
Precise iris tracking is an important tool in assistive technology, and has many advanced applications such as in human-computer interactions and driver fatigue detection. Features such as shape, colour, and size of the iris enable specific position and centre of the iris to be tracked during its movement. The iris tracking system is divided into four stages: image acquisition, face detection, eye detection, and eye tracking. This study proposes a new method for iris tracking using a generalized particle filter. This approach utilizes a sample set of the tracked iris which is created at the beginning of the tracking process. The prior representation and position of the tracked iris are then predicted depending on the minimization of parameters of the proposed generalized probabilistic distribution. Results of the experiments show that the proposed method has high accuracy and can be used to efficiently track the at a shorter length of time.
Key-Words / Index Term :
Iris tracking; Particle filter; β-Distribution; Biometrics; Fatigue detection
References :
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