Video Age Estimation with Multiple Stacked CNN Models
Automatic age classification has become relevant to an increasing amount of applications, particularly after the occurrence of many social platforms and social medias where the video age recognition is important for the improvement of user experience; however, performance of the existing methods on real-world video continuous images is in great shortage, especially when it is compared to the “super-human” improvement of recognition precision reported for the related task of object and face recognition. In this paper, we proposed a new cnn structure by combining several stacked deep-convolutional neural networks (CNN), which consist of an improved alexnet and an improved grouped googlenet. The stacked models can be used to estimate the apparent age of the people from coarse to fine. Experiments showed that a significant improvement in performance can be obtained on the video tasks. W evaluated our method on the recent benchmark for video apparent age estimation and showed it to outperform current state-of-the-art methods.