[Retracted] Age Label Distribution Learning Based on Unsupervised Comparisons of Faces

Figure 2

Flowchart of our UCLD. Our structure is divided into two stages. In the first stage, after data expansion of the image, the age samples are input into the preset CNN to get the normalized embedding of the image and then the vector embedded through the two projection layers is calculated and compared to the loss to obtain the ConAge model, which is the basis for the algorithm proposed in this paper. In the second stage, after obtaining these relevant depth features, they are projected into the average variance label distribution through a small linear layer, and the network parameters are optimized through backpropagation. At the same time, the mixed hyperparameters of the average variance label distribution are iterated through the widely used expectation-maximization optimization [16].