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Federated Learning Method for Preserving Privacy in Face Recognition System
Federated learning facilitates the training of a shared model without necessitating the sharing of individual private data, achieving this by training models on decentralized edge devices housing the data.
Unsupervised Deep Learning Image Verification Method
In this work, we propose a method to narrow this gap by leveraging an autoencoder to convert the face image vector into a novel representation.
Image Clustering using Restricted Boltzman Machine
In this work, we propose the use of RBMs to the image clustering tasks.
Autoencoder Based Face Verification System
Initially, an autoencoder is trained in an unsupervised manner using a substantial amount of unlabeled training dataset.
Deep Learning Based Face Recognition Method using Siamese Network
Achieving state-of-the-art results in face verification systems typically hinges on the availability of labeled face training data, a resource that often proves challenging to acquire in substantial quantities.
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