Face recognition using an enhanced age simulation method

We propose in this paper an enhanced age simulation method for face recognition across age differences. Since the within-class variations caused by age differences are generally much larger than the between-class variations caused by different identities at similar ages, we propose an age simulation method to reduce the facial appearance difference of the same person across age variations. Since it´s very difficult to obtain a sequence of facial images of the person at continuous ages, we first implement a filling process to enlarge our limited training database and classify it into different age groups. Then, we represent each age group by a mean feature vector. With these mean feature vectors, typical vectors for different ages are generated. Lastly, we synthesize a new virtual facial image at a target age by combining the training samples and typical feature vectors. Experimental results are presented to demonstrate the effectiveness of the proposed method.