An analysis-by-synthesis method based on sparse representation for heterogeneous face biometrics

In recent years, single-mode face recognition has achieved great progress under the impetus of a variety of applications, and its recognition rate can reach as high as 99%. Heterogeneous face biometrics, namely face mapping between images captured in different spectral bands, has now become a hotspot. Our paper focuses on the problem of face recognition between registered visual (VIS) images and predicted near infrared (NIR) images, and proposes a new method, based on extant VIS and NIR images sets to synthesize corresponding VIS image by analyzing the imaging model of heterogeneous image pairs, thereby performing the eclectic face mapping. Experiments show that the synthesized images greatly reduce the difference between them, and face recognition based on it shows promising results.