Sıkıştırılmış algılama ve piramit işlemeye dayalı yüz ifade tanıma

Year 2017, Volume: 23 Issue: 5, 527 - 531, 20.10.2017

Alaa Eleyan , Abubakar Ashir

Abstract

Bu
makalede, geliştirilmiş yüz ifadesi tanıma için yeni bir yaklaşım önerilmiştir.
Bu yeni yaklaşım sıkıştırma algılama teorisinden ve yüz ifadesi problemine
çoklu çözünürlük yaklaşımından esinlenmektedir. Başlangıçta, her bir görüntü
örneği farklı boyutlarda ve çözünürlüklerdeki piramitlerin istenilen seviyesine
ayrıştırılmaktadır. Piramidin her seviyesinde, özellikler sıkıştırma algılama
teorisine dayanan bir ölçüm matrisi kullanılarak ayrıştırılmaktadır. Bu ölçümlerin
tamamı orijinal görüntü için bir özellik vektörü oluşturmak için bir araya
getirilmektedir. Üç uzaklık ölçümü sınıflandırıcısı (Manhattan, Öklid, kosinüs)
ve destek vektör makinesi kullanımından elde edilen sonuçlar, aynı veri
tabanları ve ayarlarının kullanıldığı literatürdeki benzer algoritmaların
çoğundan daha etkileyici ve iyidir.

Keywords

Yüz İfade tanıma, Sıkıştırma algılama, Görüntü piramidi

Facial expression recognition based on compressive sensing and pyramid processing

Abstract

In
this paper, a new approach has been proposed for improved facial expression
recognition. The new approach is inspired by the compressive sensing theory and
multi-resolution approach to facial expression problems. Initially, each image
sample is decomposed into desired levels of its pyramids at different sizes and
resolutions. At each level of the pyramid, features are extracted using a
measurement matrix based on compressive sensing theory. These measurements are
concatenated together to form a feature vector for the original image. The
results obtained from the approach using three distance measurement classifiers
(Manhattan, Euclidean, Cosine) and support vector machine are impressive and
outperforms most of its counterpart algorithms in the literature using the same
databases and settings.

Keywords

Facial Expression recognition, Compressive sensing, Image pyramid

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