Journal ArticleDOI
Benchmarking lightweight face architectures on specific face recognition scenarios
Yoanna Martínez-Díaz,Miguel Nicolás-Díaz,Heydi Méndez-Vázquez,Luis S. Luevano,Leonardo Chang,Miguel González-Mendoza,Luis Enrique Sucar +6 more
TLDR
This paper studies the impact of lightweight face models on real applications and evaluates the performance of five recent lightweight architectures on five face recognition scenarios: image and video based face recognition, cross-factor and heterogeneous face Recognition, as well as active authentication on mobile devices.Abstract:
This paper studies the impact of lightweight face models on real applications. Lightweight architectures proposed for face recognition are analyzed and evaluated on different scenarios. In particular, we evaluate the performance of five recent lightweight architectures on five face recognition scenarios: image and video based face recognition, cross-factor and heterogeneous face recognition, as well as active authentication on mobile devices. In addition, we show the lacks of using common lightweight models unchanged for specific face recognition tasks, by assessing the performance of the original lightweight versions of the lightweight face models considered in our study. We also show that the inference time on different devices and the computational requirements of the lightweight architectures allows their use on real-time applications or computationally limited platforms. In summary, this paper can serve as a baseline in order to select lightweight face architectures depending on the practical application at hand. Besides, it provides some insights about the remaining challenges and possible future research topics.read more
Citations
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Proceedings ArticleDOI
MixFaceNets: Extremely Efficient Face Recognition Networks
TL;DR: MixFaceNets as discussed by the authors is a set of extremely efficient and high throughput models for accurate face verification, which are inspired by Mixed Depthwise Convolutional Kernels (MDCK).
Proceedings ArticleDOI
MFR 2021: Masked Face Recognition Competition
Fadi Boutros,Naser Damer,Jan Niklas Kolf,Kiran B. Raja,Florian Kirchbuchner,Raghavendra Ramachandra,Arjan Kuijper,Pengcheng Fang,Chao Zhang,Fei Wang,David Montero,Naiara Aginako,Basilio Sierra,Marcos Nieto,Mustafa Ekrem Erakin,Ugur Demir,Hazim Kemal Ekenel,Asaki Kataoka,Kohei Ichikawa,Shizuma Kubo,Jie Zhang,Mingjie He,Dan Han,Shiguang Shan,Klemen Grm,Vitomir Struc,Sachith Seneviratne,Nuran Kasthuriarachchi,Sanka Rasnayaka,Pedro C. Neto,Ana F. Sequeira,Joao Ribeiro Pinto,Mohsen Saffari,Jaime S. Cardoso +33 more
TL;DR: The Masked Face Recognition Competition (MFR) as discussed by the authors was held within the 2021 International Joint Conference on Biometrics (IJCB 2021) and attracted a total of 10 participating teams with valid submissions.
Posted Content
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation
TL;DR: In this article, a new family of face recognition models, namely PocketNet, is proposed to enhance the verification performance of the compact model by presenting a novel training paradigm based on knowledge distillation.
Journal ArticleDOI
PocketNet: Extreme Lightweight Face Recognition Network Using Neural Architecture Search and Multistep Knowledge Distillation
TL;DR: In this article , a novel training paradigm based on knowledge distillation is proposed, where the knowledge is distilled from the teacher model to the student model at different stages of the training maturity.
Proceedings ArticleDOI
QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization
TL;DR: QuantFace reduces the required computational cost of the existing face recognition models without the need for designing a particular architecture or accessing real training data, and intro-duces privacy-friendly synthetic face data to the quantization process to mitigate potential privacy concerns and issues related to the accessibility to realTraining data.
References
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Journal ArticleDOI
On Fisher vector encoding of binary features for video face recognition
Yoanna Martínez-Díaz,Noslen Hernández,Rolando J. Biscay,Leonardo Chang,Heydi Méndez-Vázquez,L. Enrique Sucar +5 more
TL;DR: A new way for obtaining FV encoding of binary features that is still efficient and also accurate, and shows that BRIEF combined with FV are discriminative enough, and provide as good performance as the one obtained by using SIFT features for video face recognition.
Posted Content
AirFace: Lightweight and Efficient Model for Face Recognition
TL;DR: This paper proposes a novel loss function named Li-ArcFace based on ArcFace that takes the value of the angle through a linear function as the target logit rather than through cosine function, which has better convergence and performance on low dimensional embedding feature learning for face recognition.
Proceedings ArticleDOI
Toward More Realistic Face Recognition Evaluation Protocols for the YouTube Faces Database
Yoanna Martínez-Díaz,Heydi Méndez-Vázquez,Leyanis Lopez-Avila,Leonardo Chang,L. Enrique Sucar,Massimo Tistarelli +5 more
TL;DR: This work proposes new relevant evaluation protocols for the YouTube Faces database (REP-YTF) supporting face verification and open/closed-set identification and provides an extensive experimental evaluation, by combining several well-established feature representations with three different metric learning algorithms.
Book ChapterDOI
Trends in Machine and Human Face Recognition
TL;DR: This chapter reviews two aspects of FR: machine recognition of faces and how human beings recognize human faces and the recent benchmark studies, their protocols and databases, and psychophysical studies of FR abilities of human beings.
Proceedings ArticleDOI
AirFace:Lightweight and Efficient Model for Face Recognition
TL;DR: Li-ArcFace as discussed by the authors takes the value of the angle through a linear function as the target logit rather than through cosine function, which has better convergence and performance on low dimensional embedding feature learning for face recognition.