Estimation of human age by features of face and eyes based on multilevel feature convolutional neural network

Age estimation can be effectively used in security, human-computer interaction, entertainment, and audio-visual fields. However, current face-based age recognition algorithms are not highly accurate due to factors, such as face makeup and plastic surgery. Among the various feature points of a human face, the eyes are the most difficult part to be modified. And with the changes of age, the lens, cornea, and vitreous of eyes will also change accordingly. Therefore, we believe that paying attention to the local feature of the eye can improve the accuracy of age estimation to a certain extent. The multilevel feature convolutional neural network (MLFCNN) is proposed, which values eyes features and combines them with face features to jointly estimate human age. MLFCNN performs two rounds of estimation based on extracted features. First, the age range of the sample is estimated as the age group, and then on this basis, the fine age of the sample is further estimated. Field tests show that the mean absolute error of MLFCNN is 2.87, which is lower than other network models tested. When the MLFCNN estimated age did not differ more than 4 years from the actual age of the sample, the network predictions were considered correct (i.e., the tolerable age error was 4). The age estimation accuracy of MLFCNN under this condition was 91.14%. And 98.32% accuracy can be reached when the tolerable age error is 6. Ablation experiments verify that the fusion of ocular features and facial features can improve the performance of the age estimation network. In addition, it can still maintain a good performance under small training dataset.