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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.

Privacy plays an important role in biometric authentication systems. Touch authentication systems have been widely used since touch devices reached their current level of development. In this work, a fuzzy commitment scheme (FCS) is proposed based on deep learning (DL) to protect the touch-gesture template in a touch authentication system. The binary Bose–Ray-Chaudhuri code (BCH) is used with FCS to deal with touch variations. The BCH code is described by the triplet (n, k, t) where n denotes the code word’s length, k denotes the length of the key and t denotes error-correction capability. In our proposed system, the system performance is investigated using different lengths k. The learning-based approach is applied to extract touch features from raw touch data, as the recurrent neural network (RNN) is used based on a convolutional neural network (CNN). The proposed system has been evaluated on two different touch datasets: the Touchalytics dataset and BioIdent dataset. The best results obtained were with a key length k = 99 and n = 255; the false accept rate (FAR) was 0.00 and false reject rate (FRR) was 0.5854 for the Touchalytics dataset, while the FAR was 0.00 and FRR was 0.5399 with the BioIdent dataset. The FCS shows its effectiveness in dynamic authentication systems, as good results are obtained and compared with other works.