Kenan DONUK, Ali ARI, Mehmet Fatih ÖZDEMİR, Davut HANBAY

BPSO ve SVM'ye Dayalı Yüzde Duygu Tanıma için Derin Özellik Seçimi

Günlük hayatımızda önemli sosyal iletişim aracı olan yüz ifadeleri, insanların ruhsal durumu hakkında önemli bilgiler vermektedir. Bu bilgiyi doğru bir şekilde elde etmek için araştırmalar yapılmaktadır. Bu araştırmaların insan-bilgisayar etkileşimi alanındaki önemi giderek artmaktadır. Nötr, mutluluk, şaşkınlık, üzüntü, öfke, iğrenme, korku gibi evrensel yüz ifadelerinin akıllı sistemler tarafından yüksek doğrulukla tanınması için birçok yöntem kullanılmıştır. Duygu tanıma, ortam ışığı, yaş, ırk, cinsiyet ve yüz pozisyonu gibi faktörler nedeniyle zorlu bir sınıflandırma örneğidir. Bu makalede, yüz görüntülerinden duygu tanıma için 3 aşamalı bir sistem önerilmiştir. İlk aşamada, tasarlanan CNN tabanlı ağ Fer+ veri seti ile eğitiliyor. İkinci aşamada, eğitilmiş olan CNN ağının tam bağlı katmanındaki özellik vektörüne özellik seçimi için İkili Parçacık Sürü Optimizasyon algoritması uygulanıyor. Seçilen özellikler Destek Vektör Makinesi tarafından sınıflandırılır. Önerilen sistemin performansı Fer+ veri seti ile test edilmiştir. Test sonucunda %85,74 doğruluk ölçülmüştür. Elde edilen sonuçlar İkili Parçacık Sürü Optimizasyon algoritması ve Destek Vektör Makinesi birleşiminin FER+ veri setinin sınıflandırma doğruluğuna ve hızına katkısını ortaya koymuştur.

Anahtar Kelimeler:

Yüz duygu tanıma, evrişimsel sinir ağı, ikili parçacık sürü optimizasyonu, destek vektör makinesi

Deep Feature Selection for Facial Emotion Recognition Based on BPSO and SVM

Facial expressions, which are important social communication tools in our daily life, provide important information about the mental state of people. Research is being done to obtain this information accurately. The importance of these researchs in the field of human-computer interaction is increasing. Many methods have been used for the recognition of universal facial expressions such as neutral, happiness, surprise, sadness, anger, disgust, and fear by intelligent systems with high accuracy. Emotion recognition is an example of difficult classification due to factors such as ambient light, age, race, gender, and facial position. In this article, a 3-stage system is proposed for emotion detection from facial images. In the first stage, the CNN-based network is trained with the Fer+ dataset. The Binary Particle Swarm Optimization algorithm is applied for feature selection to the feature vector in the fully connected layer of the CNN network trained in the second stage. Selected features are classified by Support Vector Machine. The performance of the proposed system has been tested with the Fer+ dataset. As a result of the test, 85.74% accuracy was measured. The results show that the combination of BPSO and SVM contributes to the classification accuracy and speed of the FER+ dataset.

Keywords:

Facial emotion recognition, convolutional neural network, binary particle swarm optimization, support vector machine,

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