Abstract
The present paper suggests a hybrid solution to address two key authentication challenges: data privacy and the limitations of mobile devices resources. The former is addressed using Partially Homomorphic Encryption based on Paillier Algorithm for the encryption. While the latter is handled using a combination of Deep Convolutional Neural Network and Local Ternary Pattern for face recognition. We compare the accuracy and performance of our proposed solution to others proposed by the literature on ORL dataset and Extended Yale data set. Our findings suggest our proposed methods return higher recognition rates including 98.75% on encrypted ORL data set and 98.78% on encrypted Extended Yale data. In contrast, the existing methods achieved lower recognition rates, which have achieved only 92.50% and 95.44% of recognition rates on encrypted ORL and Extended Yale data set, respectively.
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