Scale-Varying Triplet Ranking with Classification Loss for Facial Age Estimation
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Korea Advanced Institute of Science and Technology (KAIST)
In the field of age estimation, CNNs have been widely exploited in a variety of different approaches. One of them is simple classification. However, the classification loss, i.e. cross-entropy loss does not reflect the ordinal characteristics of age labels; it focuses on whether the predicted label is correct, but does not care about the degree of error between a prediction and its target value. To address the issue, we take a feature learning approach by an end-to-end learning objective for CNN, which is configured jointly from the proposed ranking constraint as well as the classification loss. Figure 1 shows the overall framework of our method.
By applying our method we can gather better features from face images (see figure 2), and better age estimation results.