Face recognition is an emerging field due to the technological advances in camera hardware and for its application in various fields such as the commercial and security sector. Although the existing works in 3D face recognition perform well, a similar experiment setting across classifiers is hard to find, which includes the Random Forest classifier. The aggregations of the classification from each decision tree are the outcome of Random Forest. This paper presents 3D facial recognition using the Random Forest method using the BU-3DFE database, which consists of basic facial expressions. The work using other classifiers such as Neural Network (NN) and Support Vector Machine (SVM) using a similar experiment setting also presented. As for the results, the Random Forest approach has yield 94.71% of recognition rate, which is an encouraging result compared to NN and SVM. In addition, the experiment also yields that fear expression is unique to each human due to a high confidence rate (82%) of subjects with fear expression. Therefore, a lower chance to be mistakenly recognized someone with a fear expression.
Random Forest and SVM Based Face Recognition Using Subspace Methods