We present a method for distinguishing human face from high-emulation mask, which is increasingly
used by criminals for activities such as stealing card numbers and passwords on ATM. Traditional
facial recognition technique is difficult to detect such camouflaged criminals. In this paper, we use the
high-resolution hyperspectral video capture system to detect high-emulation mask. A RGB camera is
used for traditional facial recognition. A prism and a gray scale camera are used to capture spectral
information of the observed face. Experiments show that mask made of silica gel has different spectral
reflectance compared with the human skin. As multispectral image offers additional spectral
information about physical characteristics, high-emulation mask can be easily recognized.
We present a new hybrid camera system based on spatial light modulator (SLM) to capture texture-adaptive
high-resolution hyperspectral video. The hybrid camera system records a hyperspectral video with low spatial resolution
using a gray camera and a high-spatial resolution video using a RGB camera. The hyperspectral video is subsampled by
the SLM. The subsampled points can be adaptively selected according to the texture characteristic of the scene by
combining with digital imaging analysis and computational processing. In this paper, we propose an adaptive sampling
method utilizing texture segmentation and wavelet transform (WT). We also demonstrate the effectiveness of the
sampled pattern on the SLM with the proposed method.