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The security of liveness detection in face recognition is a crucial issue, but many attacks can spoof current face feature techniques. To enhance the security of liveness detection, a method is proposed to extract human physiological components from the object and classify the properties. The proposed method, different from traditional camera-based methods that require specific movement of the human face, separates the heart rate (HR) components from the computational ghost imaging (CGI) signal and achieves liveness detection by capturing only one image rather than image sequences. The correct rate reaches 96.0% against picture attacks and mask attacks. The average error is only 3.57% compared to commercial contact HR measuring devices. Meanwhile, this method is found resolution-independent and can work in low-resolution conditions, which is experimentally verified at a resolution of 32 × 32 pixels. This method can enhance the security of liveness detection and provide a fresh framework for physiological measurements. [ABSTRACT FROM AUTHOR]

Single-pixel imaging (SPI), which is generally based on computational imaging, has the advantages of a wide bandwidth and the ability to image objects beyond the visual field. However, the major challenge in developing SPI is the large number of illumination patterns that are required. Unlike traditional SPI that relies on random measurement patterns, the SPI method proposed in this letter involves a two-step phase shift that reduces greatly the required number of illumination patterns. Theoretical analysis shows that 6724 illumination patterns are required to reconstruct a 128 × 128-pixel image whose peak signal-to-noise ratio exceeds 30, and these can be projected in 0.3362 s with a digital micromirror device working at full speed. Compared to SPI with a four-step phase shift, half the number of illumination patterns are required. Verification experiments show that the reconstructed images can be obtained even at a sampling ratio of 20%. The proposed SPI with a two-step phase shift is an effective means of requiring fewer illumination patterns and has great potential in dynamic detection. [ABSTRACT FROM AUTHOR]