The traditional age estimation methods based on ranking and regression cannot effectively utilize the evolutionary characteristics of human faces and build correlation between different ranking labels. Moreover， using binary classification methods for age estimation may result in inconsistent ranking issues. To solve above problems， an age estimation method based on integrated ranking matrix encoding and consistency preserving was proposed to fully utilize the correlation between age and ranking value and suppress the problem of inconsistent ranking. A new indicator， the proportion of samples with inconsistent ranking， was proposed to evaluate the problem of inconsistent rankings in the two-class ranking method. First， age categories were converted into a ranking matrix form through a designed coding method. Then， the ResNet34 （Residual Network） feature extraction network was used to extract facial features， which were then learned through the proposed encoding learning module. Finally， the network prediction results were decoded into the predicted age of the image through a ranking decoder based on a metric method. The experimental results show that： the proposed method achieves a Mean Absolute Error （MAE） of 2.18 on MORPH Ⅱ dataset， and has better results on other publicly available datasets compared to methods also based on ranking and ordinal regression， such as OR-CNN （Ordinal Regression with CNN） and CORAL （COnsistent RAnk Logits）； at the same time， the proposed method decreases the proportion of samples with inconsistent ranking， and improves the measurement performance of ranking inconsistency by about 65% compared to the OR-CNN method.

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.