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Online Learning and Saliency Effects On CNN-Based Gait Recognizers


Affiliations
1 System and Computer Engineering Dep., Al-Azhar University, Cairo-Egypt., Egypt
2 College of Computer and Information Sciences, Jouf University, Sakaka 72314, Saudi Arabia., Saudi Arabia
 

Authentication through gait analysis offers a reliable and an easy-to-use alternative to common authentication methods. This paper presents a novel gait recognizer that exploits online learning in Convolutional Neural Network, CNN. The features which make that algorithm promising are its high recognition accuracy and low computational cost, in addition to its adaptability, flexibility and applicability. Also, in a parallel line the effect of saliency as a means to generate global features is examined.

In this paper, the inertial measures (instead of visual data) are utilized for person authentication. Thus the smartphone inertial sensors are used to continuously assess whether the mobile is actually in the hands of the right owner or not. Three different approaches (saliency detection, offline, and online learning) have been proposed, examined, and implemented. The last two of these approaches are based on the use of convolutional neural networks to shift the measured values of the sensors into a new vector that can be classified more reliably, while the first approach is based on the use of saliency detection algorithms to get the most salient regions of the gait. The models of these approaches are carried out and various experiments on such models are conducted. The results of these experiments were promising and showed the applicability of gait recognition to provide implicit continuous authentication, specially, when online learning is relied upon, since the identification accuracy reaches 98.7%.


Keywords

Continuous Authentication, Gait Recognition, Saliency, Convolutional Neural Network, Online Learning.
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  • Online Learning and Saliency Effects On CNN-Based Gait Recognizers

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Authors

Hozaifa H. Daoud
System and Computer Engineering Dep., Al-Azhar University, Cairo-Egypt., Egypt
Abdurrahman A. Nasr
System and Computer Engineering Dep., Al-Azhar University, Cairo-Egypt., Egypt
Mohamed M. Ezz
College of Computer and Information Sciences, Jouf University, Sakaka 72314, Saudi Arabia., Saudi Arabia
Mohamed Z. Abdulmaged
College of Computer and Information Sciences, Jouf University, Sakaka 72314, Saudi Arabia., Saudi Arabia

Abstract


Authentication through gait analysis offers a reliable and an easy-to-use alternative to common authentication methods. This paper presents a novel gait recognizer that exploits online learning in Convolutional Neural Network, CNN. The features which make that algorithm promising are its high recognition accuracy and low computational cost, in addition to its adaptability, flexibility and applicability. Also, in a parallel line the effect of saliency as a means to generate global features is examined.

In this paper, the inertial measures (instead of visual data) are utilized for person authentication. Thus the smartphone inertial sensors are used to continuously assess whether the mobile is actually in the hands of the right owner or not. Three different approaches (saliency detection, offline, and online learning) have been proposed, examined, and implemented. The last two of these approaches are based on the use of convolutional neural networks to shift the measured values of the sensors into a new vector that can be classified more reliably, while the first approach is based on the use of saliency detection algorithms to get the most salient regions of the gait. The models of these approaches are carried out and various experiments on such models are conducted. The results of these experiments were promising and showed the applicability of gait recognition to provide implicit continuous authentication, specially, when online learning is relied upon, since the identification accuracy reaches 98.7%.


Keywords


Continuous Authentication, Gait Recognition, Saliency, Convolutional Neural Network, Online Learning.

References