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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Karadeniz, Mehmet Fatih
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Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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IMPROVING CNN FEATURES FOR FACIAL EXPRESSION RECOGNITION Karadeniz, Ahmet Serdar; Karadeniz, Mehmet Fatih; Weber, Gerhard Wilhelm; Husein, Ismail
ZERO: Jurnal Sains, Matematika dan Terapan Vol 3, No 1 (2019): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (375.735 KB) | DOI: 10.30829/zero.v3i1.5881

Abstract

Abstract Facial expression recognition is one of the challenging tasks in computervision. In this paper, we analyzed and improved the performances bothhandcrafted features and deep features extracted by Convolutional NeuralNetwork (CNN). Eigenfaces, HOG, Dense-SIFT were used as handcrafted features.Additionally, we developed features based on the distances between faciallandmarks and SIFT descriptors around the centroids of the facial landmarks,leading to a better performance than Dense-SIFT. We achieved 68.34 % accuracywith a CNN model trained from scratch. By combining CNN features withhandcrafted features, we achieved 69.54 % test accuracy.Key Word: Neural network, facial expression recognition, handcrafted features
Co-Authors Ismail Husein, Ismail Karadeniz, Ahmet Serdar Weber, Gerhard Wilhelm