Jinho Han*
CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3747-3759, 2022, DOI:10.32604/cmc.2022.023683
Abstract Convolutional Neural Networks (CNN) have been successfully employed in the field of image classification. However, CNN trained using images from several years ago may be unable to identify how such images have changed over time. Cross-age face recognition is, therefore, a substantial challenge. Several efforts have been made to resolve facial changes over time utilizing recurrent neural networks (RNN) with CNN. The structure of RNN contains hidden contextual information in a hidden state to transfer a state in the previous step to the next step. This paper proposes a novel model called Hidden State-CNN (HSCNN). This adds to CNN a… More >
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