Combining Tensor Space Analysis and Active Appearance Models for Aging Effect Simulation on Face Images

Abstract

Applications of the simulation of adult aging effects are widespread nowadays, whereas the difficulties in certain aspects restrict its development. In this paper, a method is proposed for simulating adult facial aging effects by means of super-resolution. Accounting for the nature of multimodalities in the face image set, multilinear algebra is introduced to represent and process the whole image set in tensor space. To ameliorate the aging simulation results generated by merely the super-resolution method, we further adopt active appearance models to reduce the blurring effects of the results through adding normalization of the faces and postprocessing to the algorithm. To evaluate our aging simulation method, the aged faces obtained are compared with the ground-truth face images of the same individuals and also assessed by several volunteers mainly from two perspectives: the aged faces' perceived age and their preservation effects of the original identities of subjects in the test images. Additionally, objective experiments based on an automatic age estimator and a face recognition method using eigenfaces are also conducted as another way of the evaluation.