LBPH based improved face recognition at low resolution

Automatic individual face recognition is the most challenging query from the past decade in computer vision. However, the law enforcement agencies are inadequate to identify and recognize any person through the video monitoring cameras further efficiently; the blur conditions, illumination, resolution, and lighting are still the major problems in face recognition. Our proposed system operates better at the minimum low resolution of 35px to identify the human face in various angles, side poses and tracking the face during human motion. We have designed the dataset (LR500) for training and classification. This paper employs the Local Binary Patterns Histogram (LBPH) algorithm architecture to address the human face recognition in real time at the low level of resolution.