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A Review of Deep Convolutional Neural Networks in Mobile Face Recognition
With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development
The Devil of Face Recognition is in the Noise
The growing scale of face recognition datasets empowers us to train strong
convolutional networks for face recognition. While a variety of architectures
and loss functions have been devised, we still have a limited understanding of
the source and consequence of label noise inherent in existing datasets. We
make the following contributions: 1) We contribute cleaned subsets of popular
face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new
large-scale noise-controlled IMDb-Face dataset. 2) With the original datasets
and cleaned subsets, we profile and analyze label noise properties of MegaFace
and MS-Celeb-1M. We show that a few orders more samples are needed to achieve
the same accuracy yielded by a clean subset. 3) We study the association
between different types of noise, i.e., label flips and outliers, with the
accuracy of face recognition models. 4) We investigate ways to improve data
cleanliness, including a comprehensive user study on the influence of data
labeling strategies to annotation accuracy. The IMDb-Face dataset has been
released on https://github.com/fwang91/IMDb-Face.Comment: accepted to ECCV'1
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