Abstract:
With the rapid development of deep learning, the accuracy of face recognition has significantly increased. However, training a face recognition model requires the collect...Show MoreMetadata
Abstract:
With the rapid development of deep learning, the accuracy of face recognition has significantly increased. However, training a face recognition model requires the collection of private data to a centralized server to obtain high performance in the desired domain. Since federated learning is a technique to train a model without collecting data to a server, it is a suitable architecture to train a face recognition model with privacy-sensitive face images held in personal smartphones. This study proposes strategies to apply federated learning to face recognition model training.
Date of Conference: 10-12 January 2021
Date Added to IEEE Xplore: 13 May 2021
ISBN Information: