Abstract
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.
Highlights
Face recognition is one of the main applications of machine vision and plays a significant role in data security field
When experimenting with 5-fold, the average accuracy increase was equal to 1.7%
The FTSGAN could improve the image quality required for face recognition
Summary
Face recognition is one of the main applications of machine vision and plays a significant role in data security field. There are still major challenges for an uncontrolled environment, where the subjects are dynamic, and it is difficult to capture changes in the machine angle due to several reasons [1]. Face information is prone to being influenced and partially reduced in the dark environment. The influential factors such as the hairstyle and clothing can affect the face bio-information. The complex environment poses a serious challenge for both the security surveillance and daily application. The data in the visible band alone cannot provide reliable face digital information
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