Open Access

ARTICLE

Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

by Asrar Bajaber^1,*, Mai Fadel¹, Lamiaa Elrefaei²

1 Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
2 Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, 11629, Egypt

* Corresponding Author: Asrar Bajaber. Email:

(This article belongs to the Special Issue: Advances in Computational Intelligence and its Applications)

Computer Systems Science and Engineering 2022, 42(2), 465-481. https://doi.org/10.32604/csse.2022.022003

Received 25 July 2021; Accepted 13 September 2021; Issue published 04 January 2022

Abstract

Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify him. The features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction approach. In this work, we investigate the ability of Deep Learning (DL) to automatically discover useful features of touch gesture and use them to authenticate the user. Four different models are investigated Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) combined with LSTM (CNN-LSTM), and CNN combined with GRU(CNN-GRU). In addition, different regularization techniques are investigated such as Activity Regularizer, Batch Normalization (BN), Dropout, and LeakyReLU. These deep networks were trained from scratch and tested using TouchAlytics and BioIdent datasets for dynamic touch authentication. The result reported in terms of authentication accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR). The best result we have been obtained was 96.73%, 96.07% and 96.08% for training, validation and testing accuracy respectively with dynamic touch authentication system on TouchAlytics dataset with CNN-GRU DL model, while the best result of FAR and FRR obtained on TouchAlytics dataset was with CNN-LSTM were FAR was 0.0009 and FRR was 0.0530. For BioIdent dataset the best results have been obtained was 84.87%, 78.28% and 78.35% for Training, validation and testing accuracy respectively with CNN-LSTM model. The use of a learning based approach in touch authentication system has shown good results comparing with other state-of-the-art using TouchAlytics dataset.

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Citations