Implementasi Metode MTCNN (Multitask Cascaded Convolutional Neural Neteowk) Pada Sistem Absensi Berbasis Face Recognition

Mohd.Faizal Bin Laranti; Nurjaya

Authors

Mohd.Faizal Bin Laranti Universitas Pamulang
Nurjaya Universitas Pamulang

Keywords:

Facial Expression Recognition (FER), Convolutional Neural Network (CNN), Cross Dataset

Abstract

Human facial expressions can describe a person's emotions, by knowing human facial expressions, the process of recognizing human emotions will be helped. For example is to recognize individual satisfaction of a service. One method that is well-known today for facial expression recognition systems is the Convolutional Neural Network (CNN). In this study, a CNN architecture will be built which has 8 convolution layers, with a depth of 32 layers. Almost all research on facial expression recognition has used datasets of non-Indonesian races. Therefore, the authors conducted an analysis of the non-Indonesian racial dataset with the Indonesian race dataset using the cross dataset technique. In this system the self- built CNN is compared with other popular CNN architectures. The results obtained from this study are the accuracy of the test data by 91.29%, sensitivity or recall or True Positive Rate (TPR) by 91.29%, precision or Positive Predictive Value (PPV) by 91,29%, and overall accuracy by 97.51%. Therefore, with a high recall value and precision, it means that the classes in the test data are handled perfectly by the model built.

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