13,093 research outputs found
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
Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution
A master face is a face image that passes face-based identity authentication
for a high percentage of the population. These faces can be used to
impersonate, with a high probability of success, any user, without having
access to any user information. We optimize these faces for 2D and 3D face
verification models, by using an evolutionary algorithm in the latent embedding
space of the StyleGAN face generator. For 2D face verification, multiple
evolutionary strategies are compared, and we propose a novel approach that
employs a neural network to direct the search toward promising samples, without
adding fitness evaluations. The results we present demonstrate that it is
possible to obtain a considerable coverage of the identities in the LFW or RFW
datasets with less than 10 master faces, for six leading deep face recognition
systems. In 3D, we generate faces using the 2D StyleGAN2 generator and predict
a 3D structure using a deep 3D face reconstruction network. When employing two
different 3D face recognition systems, we are able to obtain a coverage of
40%-50%. Additionally, we present the generation of paired 2D RGB and 3D master
faces, which simultaneously match 2D and 3D models with high impersonation
rates.Comment: accepted for publication in IEEE Transactions on Biometrics,
Behavior, and Identity Science (TBIOM). This paper extends arXiv:2108.01077
that was accepted to IEEE FG 202
Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition
In this work, we investigate the problem of face reconstruction given a
facial feature representation extracted from a blackbox face recognition
engine. Indeed, it is a very challenging problem in practice due to the
limitations of abstracted information from the engine. We, therefore, introduce
a new method named Attention-based Bijective Generative Adversarial Networks in
a Distillation framework (DAB-GAN) to synthesize the faces of a subject given
his/her extracted face recognition features. Given any unconstrained unseen
facial features of a subject, the DAB-GAN can reconstruct his/her facial images
in high definition. The DAB-GAN method includes a novel attention-based
generative structure with the newly defined Bijective Metrics Learning
approach. The framework starts by introducing a bijective metric so that the
distance measurement and metric learning process can be directly adopted in the
image domain for an image reconstruction task. The information from the
blackbox face recognition engine will be optimally exploited using the global
distillation process. Then an attention-based generator is presented for a
highly robust generator to synthesize realistic faces with ID preservation. We
have evaluated our method on the challenging face recognition databases, i.e.,
CelebA, LFW, CFP-FP, CP-LFW, AgeDB, CA-LFW, and consistently achieved
state-of-the-art results. The advancement of DAB-GAN is also proven in both
image realism and ID preservation properties.Comment: arXiv admin note: substantial text overlap with arXiv:2003.0695
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