A problem of age recognition from a human’s face is developed with the popularization of convolutional neural networks. They make it possible to determine the specific features of faces, unseen by a human eye, and interpret them as age characteristics. Existing approaches to age recognition are analyzed. Data from existing sets for learning with subsequent correction for reducing the errors made in labels by acquisition algorithms are used. Neural networks are taught and tested using the resulting data. There is a problem with head rotation, whose solution is carried out using the images of faces rotated using the PRNet neural network.
|Number of pages||8|
|Journal||Optoelectronics, Instrumentation and Data Processing|
|Publication status||Published - 1 May 2019|
- age recognition
- computer vision
- convolutional neural network
- deep neural networks