A Novel Implementation on Local Binary Patterns and Its Application to Age Invariant Face Recognition

Abstract

Age invariant face recognition (AFIR) is important for wide range of applications based on recognition techniques used in security and secret data base, law of enforcement. It is very difficult to identify the human’s age variations for to recognizing their face at certain continuous ages. So   in order to solve the above issues, challenges and problems for face expressions, pose, we developed modified deep convolutional neural networks (DCNNs) for to identify the face variations based on age factor. The current research work presented in this paper on local binary patterns for to identify the face recognition of age invariant. The large amount of Indian databases collected for age invariant subjects to the facial aging. The presented results shows that recognition accuracy is more and execution of time for the age invariant was fast based on data set for the 36×36 and 24×24 image resolutions results were shown.