Abstract
In this thesis, we propose a constrained kernel discriminant analysis (CKDA) to realize age estimation via the ranking concept. Unlike the previous work, the design of our algorithm is based on the relative order information among the pairwise facial images. In other words, we propose to utilize the difference of the data pairs as the feature rather than the original image samples. We first extract the ranking relation via binary classification on pairwise data and then conduct CKDA to compute the ranking (ordered) value of each sample via binary classifier. We next use this ranking value to do the age estimation. In addition, we further include a constraint on original samples according to their age labels to improve the estimation accuracy. In this paper, we show that the differences of image pairs can be a discriminant feature for age estimation, and our constraint also effectively decreases the error. Experimental result shows that the performance of our method is comparable to other existing works.