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Limited By:	Author = Azimifar, Zohreh;

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Displaying Results 1 - 4 of 4 on page 1 of 1

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Authentication based on face recognition under uncontrolled conditions (2017)

Tavakolian, Niloofar; Nazemi, Azadeh; Azimifar, Zohreh

Authentication based on face recognition under uncontrolled conditions (2017)

Tavakolian, Niloofar; Nazemi, Azadeh; Azimifar, Zohreh

Abstract:

This paper describes a method to address is- sues regarding uncontrolled conditions in face recognition. This method using mask projection, extracts affecting factor from the test sample and adds it to all normal training samples then compares test sample with all synthetic affected training samples. The method has been applied for multi-factor authentication/verification based on face biometric. Obtained results indicate high accuracy in the lake of sufficient training samples for each class(single sample classes). of the claimed user.

http://doras.dcu.ie/23042/

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Quick sift(QSIFT), an approach to reduce SIFT computational cost (2018)

Fazel, Zahra; Famouri, Mahmoud; Nazemi, Azadeh; Azimifar, Zohreh

Quick sift(QSIFT), an approach to reduce SIFT computational cost (2018)

Fazel, Zahra; Famouri, Mahmoud; Nazemi, Azadeh; Azimifar, Zohreh

Abstract:

SIFT has been proven to be the most robust local rotation and illumination invariant feature descriptor. Being fully scale invariant is the most important advantage of this descriptor. The major drawback of SIFT is time complexity which prevents utilizing SIFT in real-time applications. This paper describes a method to increase the speed of SIFT feature extraction using keypoint estimation and approximation instead of keypoint calculation in various scales. This research attempts to decrease SIFT computational cost without sacrificing performance and propose quick SIFT method (QSIFT). The recent researches in this area have approved that direct feature value computation is more expensive than the value extrapolation. Consequently, the contribution of this research is to reduces the time execution without losing accuracy.

http://doras.dcu.ie/23502/

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Single sample face identification (2018)

Shahali, Fatemeh; Nazemi, Azadeh; Azimifar, Zohreh

Single sample face identification (2018)

Shahali, Fatemeh; Nazemi, Azadeh; Azimifar, Zohreh

Abstract:

This paper describes three methods to improve single sample dataset face identification. The recent approaches to address this issue use intensity and do not guarantee the high accuracy under uncontrolled conditions. This research presents an approach based on Sparse Discriminative Multi Manifold Embedding (SDMME), which uses feature extraction rather than intensity and normalization for pre-processing to reduce the effects of an uncontrolled condition such as illumination. In average this study improves identification accuracy by about 17% compared to current methods

http://doras.dcu.ie/23501/

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Single sample face identification utilizing sparse discriminative multi manifold embedding (2018)

Shahali, Fatemeh; Nazemi, Azadeh; Azimifar, Zohreh

Single sample face identification utilizing sparse discriminative multi manifold embedding (2018)

Shahali, Fatemeh; Nazemi, Azadeh; Azimifar, Zohreh

Abstract:

This paper describes three methods to improve single sample dataset face identification. The recent approaches to address this issue use intensity and do not guarantee for the high accuracy under uncontrolled conditions. This research presents an approach based on Sparse Discriminative Multi Manifold Embedding (SDMME) , which uses feature extraction rather than intensity and normalization for pre–processing to reduce the effects of uncontrolled condition such as illumination. In average this study improves identification accuracy about 17% compare to current methods

http://doras.dcu.ie/23057/

Displaying Results 1 - 4 of 4 on page 1 of 1

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2018 (3)
2017 (1)

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