Hierarchic Clustering-Based Face Enhancement for Images Captured in Dark Fields

A hierarchic clustering-based enhancement is proposed to solve the luminance compensation of face recognition in the dark field. First, the face image is divided into five levels by a clustering method. Second, the results above are mapped into three hierarchies according to the histogram thresholds. A low, a middle, and a high-intensity block are found. Third, two kinds of linear transforms are performed to the high and the low-intensity blocks. Finally, a center wrap function-based enhancement is carried out. Experiment results show our method can improve both the face recognition accuracy and image quality.