The presence of fake pictures affects the reliability of visible face images under specific circumstances. This paper presents a novel adversarial neural network designed named as the FTSGAN for infrared and visible image fusion and we utilize FTSGAN model to fuse the face image features of infrared and visible image to improve the effect of face recognition. In FTSGAN model design, the Frobenius norm (F), total variation norm (TV), and structural similarity index measure (SSIM) are employed. The F and TV are used to limit the gray level and the gradient of the image, while the SSIM is used to limit the image structure. The FTSGAN fuses infrared and visible face images that contains bio-information for heterogeneous face recognition tasks. Experiments based on the FTSGAN using hundreds of face images demonstrate its excellent performance. The principal component analysis (PCA) and linear discrimination analysis (LDA) are involved in face recognition. The face recognition performance after fusion improved by 1.9% compared to that before fusion, and the final face recognition rate was 94.4%. This proposed method has better quality, faster rate, and is more robust than the methods that only use visible images for face recognition.

Strip flatness is an important indicator of steel quality, and its detection technology has always been the focus of research at home and abroad. This paper proposes a non-contact online real-time measurement scheme for hot rolled strip steel. Our research is based on the laser triangulation method, which transforms the flatness detection into the strip surface height detection. In order to prevent errors caused by jitter during strip transportation, we have added a three-point measurement method. The flatness measurement system has carried out the system hardware design with FPGA and linear CCD as the core and the system software design with the laser spot positioning as the core. The basic technology realization process is based on FPGA as the main control and processing chip, collecting the laser reflection light signal of the measured strip steel surface through the optical system through the linear array CCD to obtain the strip surface height information. The collected signals are subjected to photoelectric conversion, filtering and amplification, and A/D conversion, and are transmitted through a network port based on UDP protocol. Finally, the spot center is positioned by comparing the selected threshold method to obtain important data for calculating the flatness information. In this study, a semiconductor laser with a wavelength of 450nm, a TCD1304AP linear CCD and an FPGA chip of the XC7A35T model produced by Xilinx were selected. In the range of 4 mm above and below the reference position, the measurement accuracy of the system can reach 10μm, which meets the measurement requirements of the measured strip elongation accuracy of 1% (when the elongation is 10~200 I) or 1 I (when the elongation is less than 1 I).