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

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.

Human-Animal Affective Robot Touch Classification Using Deep Neural Network

Touch gesture recognition is an important aspect in human-robot interaction,as it makes such interaction effective and realistic.The novelty of this study is the development of a system that recognizes human-animal affective robot touch(HAART)using a deep learning algorithm.The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015.The dataset was tested with numerous subjects performing different HAART gestures;each touch was performed on a robotic animal covered by a pressure sensor skin.A convolutional neural network algorithm is proposed to implement the touch recognition system from row inputs of the sensor devices.The leave-one-subject-out cross-validation method was used to validate and evaluate the proposed system.A comparative analysis between the results of the proposed system and the state-of-the-art performance is presented.Findings show that the proposed system could recognize the gestures in almost real time(after acquiring the minimum number of frames).According to the results of the leave-one-subject-out cross-validation method,the proposed algorithm could achieve a classification accuracy of 83.2%.It was also superior compared with existing systems in terms of classification ratio,touch recognition time,and data preprocessing on the same dataset.Therefore,the proposed system can be used in a wide range of real applications,such as image recognition,natural language recognition,and video clip classification.