Open AccessComparative Study of Different Classifiers Based on Extracted Facial Features for Supervised Age Estimation
Author(s) -
Noor Falah Hasan,
Siraj Qays Mahdi
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/745/1/012041
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , local binary patterns , histogram , histogram equalization , mean squared error , decision tree , face (sociological concept) , image (mathematics) , mathematics , statistics , social science , sociology
Estimating a person age is a major concern for many researchers due to his importance in many applications, whereas finding a person age is crucial in making specific decision. This paper conducts a comparative study between two classification-algorithms for age estimation which applied on extracted Local Binary Patterns (LBP). It was also divided dataset into 3 classes in order to improve results and increasing accuracy of system, Root Mean Squared Error, Mean Absolute Error and other parameters are used to measure the precision of the system. The proposed methodology of this work is divided into three phases. First phase consists pre-processing methods in which selected image is handled using color to gray scale image conversion and histogram equalization. Result image of which is manipulated by standard Viola-Jones algorithm to detect and crop face area from the whole image. While to ensure same size for all images, detected face image is resized to 256 × 256. Second phase tends to extract LBP features to be fed in the next classification phase. In third phase, two classifiers Logistic Model Tree (LMT) as well as Sequential Minimal Optimization (SMO) have been applied on the extracted features for age estimation.