Facial Expression Recognition Techniques and Comparative Analysis Using Classification Algorithms

Gamze Ballıkaya; Duygu Kaya

Makale detayları

Facial Expression Recognition Techniques and Comparative Analysis Using Classification Algorithms

Yazarlar:

Gamze Ballıkaya | Duygu Kaya

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Engineering

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 3, pp.596-607

Belge Tipi:

Makale

Yayın Tarihi:

9/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001299132

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 With the development of technology and hardware possibilities, it has become possible to analyze the changes that occur as a result of the reflection of emotional state on facial expression with computer vision applications. Facial expression analysis systems are used in applications such as security systems, early diagnosis of certain diseases in the medical world, human-computer interaction, safe driving. Facial expression analysis systems developed using image data consist of 3 basic stages. These; extracting the face area from the input data, extracting the feature vectors of the data and classifying the feature vectors. In this study, the features of the dataset were obtained with the AlexNet model, which is one of the deep learning models that achieved successful results in classification problems. In the study in which the comparative analysis of the obtained results is presented, accuracy of 89.7, 87.8 and 81.7 was obtained with machine learning techniques.

 With the development of technology and hardware possibilities, it has become possible to analyze the changes that occur as a result of the reflection of emotional state on facial expression with computer vision applications. Facial expression analysis systems are used in applications such as security systems, early diagnosis of certain diseases in the medical world, human-computer interaction, safe driving. Facial expression analysis systems developed using image data consist of 3 basic stages. These; extracting the face area from the input data, extracting the feature vectors of the data and classifying the feature vectors. In this study, the features of the dataset were obtained with the AlexNet model, which is one of the deep learning models that achieved successful results in classification problems. In the study in which the comparative analysis of the obtained results is presented, accuracy of 89.7, 87.8 and 81.7 was obtained with machine learning techniques.

 With the development of technology and hardware possibilities, it has become possible to analyze the changes that occur as a result of the reflection of emotional state on facial expression with computer vision applications. Facial expression analysis systems are used in applications such as security systems, early diagnosis of certain diseases in the medical world, human-computer interaction, safe driving. Facial expression analysis systems developed using image data consist of 3 basic stages. These; extracting the face area from the input data, extracting the feature vectors of the data and classifying the feature vectors. In this study, the features of the dataset were obtained with the AlexNet model, which is one of the deep learning models that achieved successful results in classification problems. In the study in which the comparative analysis of the obtained results is presented, accuracy of 89.7, 87.8 and 81.7 was obtained with machine learning techniques.

 With the development of technology and hardware possibilities, it has become possible to analyze the changes that occur as a result of the reflection of emotional state on facial expression with computer vision applications. Facial expression analysis systems are used in applications such as security systems, early diagnosis of certain diseases in the medical world, human-computer interaction, safe driving. Facial expression analysis systems developed using image data consist of 3 basic stages. These; extracting the face area from the input data, extracting the feature vectors of the data and classifying the feature vectors. In this study, the features of the dataset were obtained with the AlexNet model, which is one of the deep learning models that achieved successful results in classification problems. In the study in which the comparative analysis of the obtained results is presented, accuracy of 89.7, 87.8 and 81.7 was obtained with machine learning techniques.

Makale detayları

Facial Expression Recognition Techniques and Comparative Analysis Using Classification Algorithms

Yazarlar:

Gamze Ballıkaya | Duygu Kaya

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Engineering

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 3, pp.596-607

Belge Tipi:

Makale

Yayın Tarihi:

9/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001299132

pISSN:

2147-3129

Face Expression Analysis

Feature Extraction

Deep Learning

Convolutional Neural Network

ClassificationFace Expression Analysis

Feature Extraction

Deep Learning

Convolutional Neural Network

Classification

Sayı

Makale detayları

Yazarlar: Gamze Ballıkaya | Duygu Kaya

Yayıncı: Bitlis Eren University

Ana Başlıklar: Engineering

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 3, pp.596-607

Belge Tipi: Makale

Yayın Tarihi: 9/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001299132

pISSN: 2147-3129

Sayı

Yazarlar:

Gamze Ballıkaya | Duygu Kaya

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Engineering

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 3, pp.596-607

Belge Tipi:

Makale

Yayın Tarihi:

9/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001299132

pISSN:

2147-3129