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Deep authentication model in Mobile Cloud Computing

Security in Mobile Cloud Computing (MCC) has become mandatory nowadays. This is due to the increasing use of mobile devices for accessing various accounts (e.g. Health record, Gaming, Facebook, Gmail and so on). Many researchers proposed biometric authentication in MCC, with a classical model for training and classification like using Local Binary Pattern (LBP) for the extraction of features and Support Vector Machine (SVM) for classification and so on, Deep Convolutional Neural Network (DeepCNN) outperform classical models in a number of cases. This paper aims to propose a Deep authentication model using Biometric Face Recognition based on DeepCNN in MCC. The proposed model uses the front camera of a mobile device to take a picture of the user and upload it to the cloud for computation (features extraction using DeepCNN). Due to the huge data and complex computation in deep authentication, we propose to allocate the training process to the cloud. The proposed authentication process achieves 99.50 % of accuracy. This method is implemented in Python using Keras library for deep learning.