Adaptive deep feature learning based Softmax regressive classification for aging facial recognition

Age estimation is a difficult process as it is impacted by race, gender, internal and external characteristics. The performance of age estimation was lower in order to accurately recognize the age of face images with minimal time. In order to overcome these issues, the Adaptive Deep Recurrent Brown Boosted Softmax Regressive Classification (ADRBSRC) method is proposed for aging facial recognition. Initially, the ADRBSRC method performs preprocessing using the adaptive bilateral filtering method, which is employed to eliminate the noise in the images. The ADRBSRC approach then extracts the important features of the input facial image using Adaptive Deep Recurrent Feature Learning (ADRFL) technique. Then, this method employs an ensemble learning method called Brown Boosted Softmax Regressive Classifier (BBSRC) in which each input image is classified into multiple age group classes (i.e. childhood age, teenage, young age, middle age, and old age) by designing a strong classifier. When compared to conventional methods like ensemble learning and ensemble CNN2ELM, the experimental results of the ADRBSRC method show that it improves Recognition Accuracy (RA) by 21% and 12% in the FGNET database, 24% and 13% in the MORPH database, 23% and 14% in the AGFW database, and 25% and 19% in the CALFW database. It reduces the computational time (CT) by 12% and 19% in the FGNET database, and 13% and 21% in the MORPH database, and in the AGFW database by 12% and 20%, and by 14% and 22% in the CALFW database.