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  4. Vol 7, Issue 3, 2023

Construction and Calibration of a Stereo Vision Acquisition Platform for Multimodal Face Antispoofing

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DOI: 10.23977/acss.2023.070304 | Downloads: 18 | Views: 473

Author(s)

Zuhe Li 1, Yuhao Cui 1, Weihua Liu 2, Yongshuang Yang 1

Affiliation(s)

1 School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
2 China Mobile Research Institute, Beijing, 100053, China

Corresponding Author

Yuhao Cui

ABSTRACT

In recent years, face antispoofing (FAS) has played an important role in protecting face recognition systems from various types of attacks, and with the emergence of various large-scale face antispoofing datasets, multimodal face antispoofing algorithms have become the mainstream method in the field of FAS. Therefore, how to efficiently collect high-precision and high-resolution multimodal face images is also an important issue in the field of FAS. This paper uses the feature that multispectral data have increasingly subdivided bands compared to visible light data to analyse and identify the essential properties of objects. At the same time, we consider the real-time requirements of the human face detection task. The mode of combining three channels of visible light and one channel of infrared 960 nm is adopted to form four-channel multispectral data to obtain multidimensional information of the target and minimize band redundancy in terms of the data construction mode. Finally, we use the principle of speckle structured light to obtain the spatial three-dimensional point cloud data of the scene, and a complete scene data type is further constructed, which provides strong support for the application of multimodal face antispoofing technology.

KEYWORDS

Face anti-spoofing, multispectral data, multidimensional information, depth alignment, binocular matching

CITE THIS PAPER

Zuhe Li, Yuhao Cui, Weihua Liu, Yongshuang Yang. Construction and Calibration of a Stereo Vision Acquisition Platform for Multimodal Face Antispoofing. Advances in Computer, Signals and Systems (2023) Vol. 7: 22-32. DOI: http://dx.doi.org/10.23977/acss.2023.070304.

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