Considering the difficulty of extracting label ordinal information and inter-class correlation in facial age estimation， a Multi-Peak Distribution （MPD） age coding was proposed， and a multi-task age estimation method MPDNet （MPD Network） was constructed based on the proposed age coding. Firstly， in order to extract correlation information among age labels and construct aging trend stages， the age labels were transformed into age distributions by using MPD. Then， a lightweight network was used for multi-stage feature extraction， and Label Distribution Learning （LDL） and regression learning were performed on the extracted features respectively. Finally， the outputs of the two learning tasks were shared and optimized with each other by back-propagation during the training process， thereby avoiding the error propagation caused by the direct regression of distribution results in traditional label distribution learning. Experimental results on MORPH Ⅱ dataset show that， the Mean Absolute Error （MAE） of MPDNet reaches 2.67， which is similar to that of the methods such as DEX （Deep EXpectation） and RankingCNN （Ranking Convolutional Neural Network） built by VGGNets （Visual Geometry Group Networks）， while the parameters of MPDNet are only 1/788.6 of those of VGGNets. Meanwhile， MPDNet outperforms lightweight methods such as C3AE and SSR-Net （Soft Stagewise Regression Network）. MPDNet can better utilize the rich correlation information among age labels to extract more discriminative age features and improve the prediction accuracy of age estimation tasks.