An Intelligent Human Age Prediction from Face Image Framework Based on Deep Learning Algorithms

Abstract

Age prediction is the task of extracting features from the human face image. Human aging factors can be expressed as multifactorial, gradual, time-dependent, physical, and biological damage. Attributes are extracted from a face image, and the aging factor depends on cells, tissues, and all living organisms. Human age prediction is distinct from chronological age prediction. Each human’s biological identity has unique characteristics. Age prediction depends on the maturity process of organs, other tissues, and cells. Many research works have been done on age classification using various techniques from human face images. It is a difficult task to the analysis of facial appearance. Issues in the existing algorithm are inefficient and require more computation time and storage space. To address these issues, this paper proposed a Deep convolutional neural network (DCNN) with a Cuckoo search algorithm (DCNN-CS). In this proposed work, DCNN-CS produces an effective age prediction from the human face image within a minimum execution time, handling a large dataset. The accuracy rate of the convolutional neural network (CNN) got 81.32, the Deep Neural Network (DNN) got 82.34, the Long short-term memory (LSTM) got 88.12, and the proposed work SLSTM-DNN got 91.45.

Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models

“…Sathyavathi and Baskaran (2023) [14] do not explicitly address the issue of racial bias in age estimation models, nor do they investigate the impact of unbalanced training data on model performance and bias across different ethnicities. Their primary focus is on improving the accuracy of age prediction using a deep learning framework that combines a Deep Convolutional Neural Network (DCNN) with a Cuckoo Search (CS) algorithm.…”

“…Therefore, while Sathyavathi and Baskaran (2023) propose a method to improve the accuracy of age prediction, they do not directly address the issues of fairness and racial bias that our research aims to tackle.…”