Age invariant face recognition method based on enhanced convolutional neural network

Research on anti age invariant face recognition can not only improve the robustness of facial recognition systems, but also provide guidance for the development and application of facial recognition technology. Aiming at the problems of low peak signal-to-noise ratio, low recognition accuracy and long recognition time of traditional anti-age invariant face recognition methods, an age invariant face recognition method based on enhanced convolutional neural network is proposed. The captured images are enhanced using a bilateral filtering algorithm. The SURF algorithm is employed to extract facial features and remove age-related interference features, completing the selection of facial image features. These selected features are then inputted into the enhanced CNN to obtain the age invariant face recognition results. The experimental results demonstrate that the proposed method achieves a maximum image peak signal-to-noise ratio of 56.85 dB, varying recognition accuracy in the range of 96.1% to 97.6%, and a maximum recognition time of 78.96 ms.