Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

Face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics, and so on. It is a challenging task the field of computer vision. Although various state-of-the-art methods face constrained environments have achieved satisfactory results, there are still many issues which untouched unconstrained environments, such as partial occlusions, large pose variations, etc. In this paper, authors proposed an approach utilized local generic feature (LGF) recognize occlusion by fusing features scale invariant transform (SIFT) multi-block binary pattern (MB-LBP). also utilizes robust kernel method for classification query image. They validated effectiveness on benchmark AR database. The experimental outcomes illustrate that outperformed state-of-art recognition.