Existing face recognition systems have demonstrated success in constrained settings with limited variability in illumination, pose, and expression. However, these incremental improvements are not sufficient to transcend the challenging applications such as identifying missing persons or matching individuals with photo ID. These applications require recognition of face images with aging variations and matching digital to scanned photo images. This paper presents a preprocessing framework to enhance the quality of the input scanned and digital face images and minimize the aging differences. Three face recognition algorithms are used to evaluate the efficacy of the proposed framework. Experimental results computed on a digital and scanned database of 310 subjects show that the framework improves the accuracy of all three algorithms by minimizing the quality differences and the variations due to aging. © 2010 IEEE.