Deteksi Ekspresi Wajah Menggunakan Metode CNN. (Face Expression Detection Using CNN Methods).

Setiyadi, Dimas (2022) Deteksi Ekspresi Wajah Menggunakan Metode CNN. (Face Expression Detection Using CNN Methods). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Face Recognition is a technology that has been widely used lately. There are so many implementations that can be used for face recognition. For example, it is used for attendance, security in the health sector. This study proposes a system that can detect human expressions through their facial expressions. The primary purpose of this study is to detect a person's emotions through the face to find out what emotions are being expressed. Because this will later be implemented in an express detection tool. The method used is the CNN method. Briefly, the study started from collecting datasets obtained from 20 until 25-year olds, a total of 20 people with additional datasets obtained from the internet. The facial datasets taken include angry, happy, sad, surprised, and neutral emotions. Then the extraction process can be carried out, which will later be trained and tested to classify the type of emotion. After that, a haar cascade frontal face will be given, provided by Open CV, and serves to organize faces and non-faces. The hypotheses used include whether the number of datasets and CNN models affects detection accuracy. There are four stages of testing: in normal conditions, expression test, given an object that blocks the face, and set the light intensity. The results of this research are the system can detect the expression expressed using a model that produces a training accuracy of 0.7486; training loss 0.6540; validation accuracy is 0.7053 and validation loss is 0.7966, and detection accuracy is 73.33%, in hats by 60%, clear glasses 40%, and sunglasses 46.67%, the accuracy value obtained with the object given the challenge is 70% bright light intensity, 50% dim light, and 43.33% dark. Keywords: Face Recognition, Emotion, Expression, Haarcascade, Convolutional Neural Network

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