Gender and age classification using ASMNet based facial fiducial detection and Jordan neural network

A capacity to instantly determine someone's age and gender merely by looking at their face has made facial recognition technology essential. Among the difficulties faced by researchers in computer vision and also psychophysics are the observation of human faces and modeling of their characteristic traits. Due to inadequate face fiducial point detection and poor image quality, many of the methods that have been designed in existing models based on facial features for age and gender categorization still face some challenges. Hence, the Active Shape Model (ASMNET) based Jordan neural network was developed for facial fiducial detection. In this designed model, the facial images are considered as input. These images are pre-processed using cropping, center surrounds device normalization, optimized Gabor filter and logarithmic transformation. Based on the preprocessed data, facial fiducial points and distinct features are detected using ASMNet combined with Convolutional Neural Network. Using this primary facial detected landmarks such as eye, mouth, nose tip and lips are extracted for features using EfficientNetB7 and classified based on the Jordan neural network to categorize age and gender. Performance metrics for this designed model include Accuracy, Positive predictive value, Hit rate, Selectivity and Negative Predictive Value. The proposed models achieved performance metrics values are 93%, 87%, 89%, 94.82% and 92.32%. Gender and Age Classification using ASMNet based Facial Fiducial Detection and Jordan Neural Network is better than the existing model along with that using this prediction technique the possibility of error rate gets reduced and timely detection can be achieved.