抄録
Recently, services using facial information such as age and gender are provided widely. This paper proposes a mean to estimate age and gender at once in facial images by using novel features. First, a facial image is normalized by using Active Appearance Model (AAM), then appearance parameters are used as global feature. Next, Gabor magnitude pictures are obtained by convolving the normalized image with Gabor filters, followed by encoding with Local Directional Pattern (LDP) operator which enhances information. Then, the maps are divided into several blocks, and histograms are extracted from each block. The histograms are concatenated to a vector which is adopted as local feature. Then PCA is used to reduce the dimensions. Next, the global and local features are combined to one vector. Finally, age is estimated by using SVM and SVR, and also gender is classified by using SVM. In the experiment, the proposed age estimation method improves 0.7 or more on Mean Absolute Error (MAE), and the result of gender classification on average is 89.35% that is best performance as compared with the conventional methods. As a result, the experiment demonstrates that the proposed algorithm is an effective method, compared to the other similar methods.
