Homogeneous Low-Resolution Face Recognition Method based Correlation Features
Face recognition technology has been widely adopted in many mission-critical scenarios like means of human identification, controlled admission, and mobile device access, etc. Security surveillance is a typical scenario of face recognition technology. Because the low-resolution feature of surveillance video and images makes it difficult for high-resolution face recognition algorithms to extract effective feature information, Algorithms applied to high-resolution face recognition are difficult to migrate directly to low-resolution situations. As face recognition in security surveillance becomes more important in the era of dense urbanization, it is essential to develop algorithms that are able to provide satisfactory performance in processing the video frames generated by low-resolution surveillance cameras. This paper study on the Correlation Features-based Face Recognition (CoFFaR) method which using for homogeneous low-resolution surveillance videos, the theory, experimental details, and experimental results are elaborated in detail. The experimental results validate the effectiveness of the correlation features method that improves the accuracy of homogeneous face recognition in surveillance security scenarios.