Abstract
Face recognition is one of the most popular applications in video surveillance systems and computer vision. The researches of face recognition in recent years have been shown that their applications are widely used in practice. Particularly, during the pandemic of Covid-19, there were a lot of researches relating to face recognition with and without mask. The accuracy of the face recognition algorithms is depended on technical issues, implemented solutions and models of data processing. In this paper, we propose an improved method for face recognition based on deep learning techniques and data augmentation. Our contribution of the proposed method is focused on the following steps: (1) obtaining and pre-processing data for training dataset based on image processing techniques (i.e. noise removal, mask wearing). (2) Creating a trained model of new dataset based on the Inception Resnet-v1. (3) Building an application for face recognition in timekeeping of a company. We use the two popular face datasets which are open source and publicity available: Casia-WebFace [1] for training and LFW [2] for validation. Comparing the several methods, the accuracy of our method is higher in case with mask and the processing time is very fast in the real time.
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Nguyen, L.D.V., Van Chau, V., Van Nguyen, S. (2022). Face Recognition Based on Deep Learning and Data Augmentation. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_38
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DOI: https://doi.org/10.1007/978-981-19-8069-5_38
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