Combined Deep Learning With Directed Acyclic Graph SVM for Local Adjustment of Age Estimation

In order to further improve the accuracy of age estimation, a locally adjusted age estimation algorithm based on deep learning and directed acyclic graph SVM is proposed. In the training phase, SE-ResNet-50 network pre-trained by the VGGFace2 dataset is first fine-tuned. Once the network converges, and the vector consisting of the parameters of the last fully connected layer is used as a representation and train multiple One-Versus-One SVMs. In the test phase, we first sent the face image to be estimated into SE-ResNet-50 to obtain a rough age estimation value, then set the specific neighborhood, and finally combined the trained SVM into a directed acyclic graph SVM and set specific neighborhood with the global estimate as the center for accurate age estimate. In order to show the universality of the proposed coarse-to-fine or/and global-to-local method, experiments were carried out on MORPH and AFAD images of different races, and the results verified the effectiveness of the algorithm.