Face Age Estimation using Shortcut Identity Connection of Convolutional Neural Network

Depletion of skin and muscle tone has a considerable impact on the appearance of the face, which is constantly evolving. Algorithms necessitate a large number of aging faces for this purpose. Another popular deep learning technique is convolutional neural networks. In a recent study, many computer vision and pattern recognition problems have been successfully tackled using it. But these methods have architectural issues (e.g., the training process) that have a negative impact on their age estimation performance. As a result, a whole new approach is proposed in this research to address the issue. Using a convolutional neural network framework and resnet50 architecture, researchers were able to detect the age of a human face. This proposed shortcut identity connection strategy, which enables age estimation from the face image, has improved the success of the resnet50 architecture. To be able to tell a person's age from a picture of their face, it was important to know the characteristics of aging. As a result, the rhetorical classification method, which employs the resnet50 structure, is used to shift the face aging levels to a probability level. All of the 50 layers in the proposed residual network have a residual block that connects them. As far as face-aging databases go, ImageNet and FG-NET are both good choices for the proposed age estimation process. In the training session, the experiment results are 2.27 and 2.38, based mostly on the mean absolute error. The test accuracy results for the ImageNet dataset are 81.75% with the FG-NET dataset and 57% with the ImageNet dataset.

@article{Pramanik2022,
  title     = {Face Age Estimation using Shortcut Identity Connection of Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {4},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Shohel Pramanik and Hadi Affendy Bin Dahlan},
  doi       = {10.14569/IJACSA.2022.0130459},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130459}
}