An Approach of Detecting the Age of a Human by Extracting the Face Parts and Applying the Hierarchical Methods
- 30 June 2021
- journal article
- research article
- Published by International Information and Engineering Technology Association in Traitement du Signal
- Vol. 38 (3), 681-688
- https://doi.org/10.18280/ts.380315
Abstract
One of the key challenges that the computer vision is facing is the age prediction. A well efficient CNN is selected for age prediction by performing various CNN operations by taking the categories as age 40 and above age 40. The selected CNN method obtained a training accuracy of 100% at more than 100 epochs. Hence, 100 epochs is considered for training. At this, the validation accuracy achieved is 84.9%. Three kinds of age phases with an age gap of 20,10 and 5 are used to predict the age. The normal method results in very less accuracy. Hence a hierarchical method is formulated. Under the hierarchical method, CNN is trained to estimate the age gaps in decreasing order. Hence not a single classifier, a group of classifiers are used for testing the image. From traditional method to hierarchical method, the 20 age gap accuracy increased from 27% to above 60%, ten age gap increased from 12% to above 35%, and five age gap increased from 5.5% to above 21%. To improve further, the features of the face parts are derived and combined which improves the efficiency compared to normal method, but not good accuracy as Hierarchical method. The combination of hierarchical method along with the face feature extraction method results in a considerable improvement in accuracy.Keywords
This publication has 27 references indexed in Scilit:
- Cost-Sensitive Local Binary Feature Learning for Facial Age EstimationIEEE Transactions on Image Processing, 2015
- Relative Attribute SVM+ Learning for Age EstimationIEEE Transactions on Cybernetics, 2015
- Combining Facial Dynamics With Appearance for Age EstimationIEEE Transactions on Image Processing, 2015
- Automatic prediction of age, gender, and nationality in offline handwritingEURASIP Journal on Image and Video Processing, 2014
- Walking-Age Analyzer for Healthcare ApplicationsIEEE Journal of Biomedical and Health Informatics, 2014
- Facial Age Estimation by Learning from Label DistributionsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
- Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor CriterionIEEE Transactions on Image Processing, 2011
- Age Synthesis and Estimation via Faces: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
- Age-Invariant Face RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
- A Compositional and Dynamic Model for Face AgingIEEE Transactions on Pattern Analysis and Machine Intelligence, 2009