Age and Gender Prediction using Adaptive Gamma Correction and Convolutional Neural Network
Anweasha Saha, S Nithish Kumar, P Nithyakani · 2023
The classification of age and gender has drawn increased attention recently because of its significance in creating user-friendly intelligent systems. In the domains of image processing and computer vision, determining age from a single facial image has proven a challenging job. Convolutional Neural Network (CNN) based techniques have been frequently adopted for the classification problem in the recent past because of their precise results in facial analysis. This study incorporates an end-to-end CNN approach with the addition of a key pre-processing step for image contrast enhancement, which was done via an adaptive gamma correction technique to produce precise gender and age group classification of real-world faces. The complete feature extraction and classification processes are included in the two-level CNN architecture. The feature extraction task pulls features that are related to gender and age while the classification assigns the facial photographs to the proper gender and age group. The proposed network has been trained and tested on the Adience (original) dataset. The results of the experiment seem to back up the claim that the proposed model is better at classifying people by gender and age when the Adience benchmark for classification is used.