Objective Human age is an important feature in human recognition and search tasks. Existing research generally treats age estimation in facial images as a traditional classification task ignoring the ordered characteristics of age and resulting in a large gap between the estimated age and the actual age. Therefore it is necessary to find a method to reduce the gap between the estimated age and the actual age. Methods This paper proposed a method for age estimation of face images based on a double-ordinality constrained convolutional neural network DO-CNN model. Firstly DO-CNN used an ordered regression model based on the generalized Logistic distribution as a classifier for convolutional neural networks and verified its superiority over other ordered classifiers for face estimation tasks. Then an ordered competitive ratio loss function was further proposed. By introducing a risk term into the traditional competitive ratio loss function the loss function considered the error between the predicted age and the actual age thus guiding the model to reduce the gap between the estimated age and the actual age. Results Comparative experiments on the opensource facial image age datasets FGNET and AgeDB showed that compared with existing research methods DO-CNN improved the accuracy by about 12% and 3% respectively and this advantage remains even when allowing for a larger error range. In addition the ordered regression classifier based on the generalized Logistic distribution exhibited significant improvements compared with ordered regression classifiers based on other distributions. Conclusion The experimental results show that the convolutional neural network model based on double-ordinality constraints can significantly improve the accuracy of age estimation in facial images and reduce the actual errors in age estimation.