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Efficient smile detection by Extreme Learning Machine
Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration
Objective Classes for Micro-Facial Expression Recognition
Micro-expressions are brief spontaneous facial expressions that appear on a
face when a person conceals an emotion, making them different to normal facial
expressions in subtlety and duration. Currently, emotion classes within the
CASME II dataset are based on Action Units and self-reports, creating conflicts
during machine learning training. We will show that classifying expressions
using Action Units, instead of predicted emotion, removes the potential bias of
human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D
feature descriptors. The experiments are evaluated on two benchmark FACS coded
datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when
classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the
result of the state-of-the-art 5-class emotional-based classification in CASME
II. Results indicate that classification based on Action Units provides an
objective method to improve micro-expression recognition.Comment: 11 pages, 4 figures and 5 tables. This paper will be submitted for
journal revie
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