2026-09-21T02:57:08Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/4452 2021-12-13T01:55:19Z 1582963302567:1597824273898 user-ucsy

Automatic Age Prediction of Aging Effects on Face Images Tin, Hlaing Htake Khaung Sein, Myint Myint Automatic age prediction system for grayscale facial images is proposed in this paper. Tenage groups, including, are used in the predictionsystem. The process of the system is divided intothree phases: location, feature extraction, andage prediction. Principal Component Analysis(PCA) was used to reduce dimension andenhance class. Finally Euclidean distance wasused to classify the images into one of sevenmajor groups. These groups are: Group1 (0 to10 years), Group2 (11 to 20 years), Group3 (21to 30 years), Group4 (31 to 40 years), Group5(41 to 50 years), Group6 (51 to 60 years) andGroup7 (60 over). The proposed system isexperimented with 1300 facial images on a Core2 Duo processor with 2 GB RAM. One half of theimages are used for training and the other halffor test. It takes 0.2 second to classify an imageon an average. The identification rate achieves95.5% for the training images and 85.5% for thetest images, which is roughly close to human’ssubjective prediction. 2012-02-28 http://hdl.handle.net/20.500.12678/0000004452 https://meral.edu.mm/records/4452