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Multi-modal facial affective analysis based on masked autoencoder
Human affective behavior analysis focuses on analyzing human expressions or other
behaviors to enhance the understanding of human psychology. The CVPR 2023 …
behaviors to enhance the understanding of human psychology. The CVPR 2023 …
Transface: Calibrating transformer training for face recognition from a data-centric perspective
Edgeface: Efficient face recognition model for edge devices
In this paper, we present EdgeFace-a lightweight and efficient face recognition network
inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of …
inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of …
A survey of face recognition
Recent years witnessed the breakthrough of face recognition with deep convolutional neural
networks. Dozens of papers in the field of FR are published every year. Some of them were …
networks. Dozens of papers in the field of FR are published every year. Some of them were …
Uniface: Unified cross-entropy loss for deep face recognition
As a widely used loss function in deep face recognition, the softmax loss cannot guarantee
that the minimum positive sample-to-class similarity is larger than the maximum negative …
that the minimum positive sample-to-class similarity is larger than the maximum negative …
Unitsface: Unified threshold integrated sample-to-sample loss for face recognition
Sample-to-class-based face recognition models can not fully explore the cross-sample
relationship among large amounts of facial images, while sample-to-sample-based models …
relationship among large amounts of facial images, while sample-to-sample-based models …
Wild face anti-spoofing challenge 2023: Benchmark and results
Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of
automated face recognition systems. Despite substantial advancements, the generalization …
automated face recognition systems. Despite substantial advancements, the generalization …
A quality aware sample-to-sample comparison for face recognition
Currently available face datasets mainly consist of a large number of high-quality and a
small number of low-quality samples. As a result, a Face Recognition (FR) network fails to …
small number of low-quality samples. As a result, a Face Recognition (FR) network fails to …