LBP and SIFT based facial expression recognition

Omer Sumer; Ece Olcay Gunes

doi:10.1117/12.2181505

14 February 2015 LBP and SIFT based facial expression recognition

Omer Sumer, Ece Olcay Gunes

Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 94450A (2015) https://doi.org/10.1117/12.2181505
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy

Abstract

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

Citation Download Citation

Omer Sumer and Ece Olcay Gunes "LBP and SIFT based facial expression recognition", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94450A (14 February 2015); https://doi.org/10.1117/12.2181505

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