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.