A Comparison of Age Prediction Classifiers via Active Appear-Acne Model

 
 
 
  • Abstract
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  • Abstract


    Individual age gives key demographic data. It is viewed as a paramount delicate biometric characteristic for individual identification, contrasted with other pattern recognition issues. Age estimation is a complex issue particularly in relation to facial pictures with different ages, since the aging procedure varies extraordinarily across different age groups. In this research, we proposed deep learning algorithm for age prediction In light of Active Appearance Models (AAM) and six classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA), and Projection Twin Support Vector Machine (PTSVM) to move forward the accuracy of age prediction dependent upon the introduced strategies. In this algorithm, we extracted the features of the facial images in features vectors using AAM model, machine learning algorithms are used to predict the age. We distinguished that the precision of CCA algorithm is the best, the intermediate is SVR and the KNN algorithm is the lowest.

     

     


  • Keywords


    Age Prediction, Feature Extraction, Active Appearance Models (AAM), Age Classification.

  • References


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Article ID: 24681
 
DOI: 10.14419/ijet.v7i3.28.24681




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