Preserving Global and Local Features for Robust FaceRecognition under Various Noisy Environments

摘要

The increasing use of biometric technologies in high-security applications and beyond has stressed the requirement for highly dependable face recognition systems. Much research on face recognition considering the large variations in the visual stimulus due to illumination conditions, viewing directions or poses, facial expressions, aging, and disguises such as facial hair, glasses, or cosmetics has been done earlier. However, in reality the noises that may embed into an image document during scanning, printing or image capturing process will affect the performance of face recognition algorithms. Though different filtering algorithms are available for noise reduction, applying a filtering algorithm that is sensitive to one type of noise to an image which has been degraded by another type of noise lead to unfavourable results. In turn, these conditions stress the importance of the design of robust face recognition algorithms that retain recognition rates even under noisy conditions. In reality, many face recognition algorithms exist and produce good results for noiseless environments but not with various noises. In this work, numerous experiments have been conducted to analyze the robustness of our proposed Combined Global and Local Preserving Features (CGLPF) algorithm along with other existing conventional algorithms under different types of noises such as Gaussian noise, speckle noise, salt and pepper noise and quantization noise.