Comparison of Data Mining Techniques In Face Recognition

บทคัดย่อ

Forecasting and automatic face recognition research is challenging. Several researchers have used data miningtechniques to identify the gender from their faces. Thus, data mining techniques including Support Vector Machine (SVM), MultiLayer Perception (MLP), Naïve bay, K-Nearest Neighbor (K-NN) and Decision Trees, have played a greater role in classification problems and face recognition. In this paper, those data mining techniques are employed to recognize 15 people’s face from different angles from Yale Center for Computational Vision. Control 10-fold cross validation, precision, recall and F-measure were utilized to evaluate the performance and effectiveness of SVM, MLP, Naïve bay, K-NN and Decision Trees. The experimental results showed that MLP is superior to SVM, Naïve bay, K-NN and Decision Trees.