Search Results for author: Hitoshi Imaoka

Found 8 papers, 3 papers with code

CalibrationPhys: Self-supervised Video-based Heart and Respiratory Rate Measurements by Calibrating Between Multiple Cameras

no code implementations23 Oct 2023 Yusuke Akamatsu, Terumi Umematsu, Hitoshi Imaoka

In this paper, we propose CalibrationPhys, a self-supervised video-based heart and respiratory rate measurement method that calibrates between multiple cameras.

Contrastive Learning Data Augmentation

Blood Oxygen Saturation Estimation from Facial Video via DC and AC components of Spatio-temporal Map

no code implementations14 Dec 2022 Yusuke Akamatsu, Yoshifumi Onishi, Hitoshi Imaoka

Our method constructs CNN models that consider the direct current (DC) and alternating current (AC) components extracted from the RGB signals of facial videos, which are important in the principle of SpO2 estimation.

SpO2 estimation

Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition

no code implementations25 Apr 2022 Takaya Miyamoto, Hiroshi Hashimoto, Akihiro Hayasaka, Akinori F. Ebihara, Hitoshi Imaoka

Furthermore, comparative experiments with existing state-of-the-art HFR methods show that our method achieves a comparable HFR performance on the Oulu-CASIA NIR&VIS dataset with less degradation of VIS performance.

Face Recognition Heterogeneous Face Recognition +1

Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy

2 code implementations ICLR 2021 Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka

Classifying sequential data as early and as accurately as possible is a challenging yet critical problem, especially when a sampling cost is high.

Decision Making Density Ratio Estimation

Specular- and Diffuse-reflection-based Face Spoofing Detection for Mobile Devices

1 code implementation29 Jul 2019 Akinori F. Ebihara, Kazuyuki Sakurai, Hitoshi Imaoka

In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems.

Face Presentation Attack Detection Face Recognition

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