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

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.

Biometric Template Protection for Dynamic Touch Gestures Based on Fuzzy Commitment Scheme and Deep Learning

“…We did two experiments with two different DL architectures: CNN-LSTM and CNN-GRU [33]. After conducting several experiments with different values of the training parameters, we adjusted the values of these parameters for all datasets as follows: epochs = 100, batch size = 500, split ratio = (70% training, 10% validation, 20% testing).…”

“…The aim of this model is to authenticate the user based on his touch gestures. Many studies of touch authentication systems have extracted touch features from touch raw data using DL [32,33].…”

“…In order to extract helpful features from raw touch data, we follow a learning-based approach [33]. We build a CNN-RNN model that contains CNN and RNN layers.…”

“…We build a CNN-RNN model that contains CNN and RNN layers. The RNN layer is either long short-term memory (LSTM) or gated recurrent unit (GRU) [33]. The input to these models is users' raw touch data, and the output is classified class y with probability p. Table 1 presents the model structures used in the touch authentication system.…”