Facial Emotion Recognition Using Active Shape Models and Statistical Pattern Recognizers

This paper investigates various emotion recognition techniques from the facial expression of human subjects. To describe human facial expressions, a number of characteristic points are extracted from input face images using active shape models (ASMs), and translated 49 scalar features so that they a...

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Bibliographic Details
Published in2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications pp. 514 - 517
Main Authors Gil-Jin Jang, Jeong-Sik Park, Jo, Ahra, Ji-Hwan Kim
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
Active shape model
ASM (activa shape model)
cross validation
Educational institutions
Emotion recognition
facial expression
Feature extraction
pattern recognition
Support vector machines
SVM (support vector machine)
Training
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Summary:This paper investigates various emotion recognition techniques from the facial expression of human subjects. To describe human facial expressions, a number of characteristic points are extracted from input face images using active shape models (ASMs), and translated 49 scalar features so that they are invariant to scale and position changes. The scalar feature values then construct a 49-dimensional feature vector for each still image. Statistical pattern recognizers, such as support vector machine (SVM) and multi-layer perceptron (MLP), are used to identify various emotions from the feature vectors. To analyze the performances of the various pattern recognizers on the limited amount of image data, 5-fold cross-validation is carried out, with varying numbers of emotions from 3 to 6. Evaluation results show that SVM is the most stable and best in terms of emotion classification rates.
DOI:10.1109/BWCCA.2014.110