Facial Age Estimation with a Hybrid Model

How to accurately estimate facial age is still an intractable task because of insufficiency of training data. In this paper, a hybrid model is proposed to estimate facial age by means of extreme learning machine (ELM) and label distribution support vector regressor (LDSVR). In the proposed method, the bio-inspired features are adopted to estimate the facial age due to its prominent performance. In order to improve the accuracy and decrease the computation burden, the ratio of feature’s between-category to within-category sums of squares (BW) is designed as a criterion to select features. To define the category of each sample, the training data is divided into several sets according to age group. Different virtual class labels are assigned to the samples of each set, respectively. Given the reduced data, a multiple-input-single-output ELM regression model is established to estimate the facial ages. Moreover, a label distribution support vector regressor is adopted to estimate facial age based on a multiple-input-multiple-output regression model. After obtaining the outputs of ELM and LDSVR, a linear weighting strategy is devised to compute the final estimation of facial age. Experimental results on a well known facial image database demonstrates the feasibility and efficiency of the proposed hybrid model.