Feature Extraction and Feature Selection for Emotion Recognition using Facial Expression
- 1 September 2020
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Facial expressions play a significant role in describing the emotions of a person. Due to its applicability to a wide range of applications, such as human-computer interaction, driver status monitoring, etc. Facial Expression Recognition (FER) has received substantial attention among the researchers. According to the earlier studies, a small feature set is used for the extraction of facial features for FER system. To date, a systematic comparison of the facial features does not exist. Therefore, in the current research, we identified 18 different facial features (cardinality of 46,352) by reviewing 25 studies and implemented them on the publicly available Extended-Cohn-Kanade (CK+) dataset. After extracting facial features, we performed Feature Selection (FS) using Joint Mutual Information (JMI), Conditional Mutual Information Maximization (CMIM) and MaxRelevance Min-Redundancy (MRMR) and explain the systematic comparison between them, and for classification, we applied various machine learning techniques. The Bag of Visual Words (BoVW) model approach results in significantly higher classification accuracy over the formal approach. Also, we found that the optimal classification accuracy for FER can be obtained by using only 20% of the total identified features. Grey comatrix and haralick features were explored for the first time for the FER and grey comatrix feature outperformed several most commonly used features Local Binary Pattern (LBP) and Active Appearance Model (AAM). Histogram of Gradients (HOG) turns out to be the most significant feature for FER followed Local Directional Positional Pattern (LDSP) and grey comatrix.Keywords
This publication has 46 references indexed in Scilit:
- Bag-of-Words Representation in Image Annotation: A ReviewISRN Artificial Intelligence, 2012
- Face recognition using Histograms of Oriented GradientsPattern Recognition Letters, 2011
- Recognition of Facial Expressions with Principal Component Analysis and Singular Value DecompositionInternational Journal of Computer Applications, 2010
- Understanding bag-of-words model: a statistical frameworkInternational Journal of Machine Learning and Cybernetics, 2010
- Facial expression recognition based on shape and texturePattern Recognition, 2009
- Face recognition using HOG–EBGMPattern Recognition Letters, 2008
- Texture and shape information fusion for facial expression and facial action unit recognitionPattern Recognition, 2008
- The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathologyDevelopment and Psychopathology, 2005
- Image analysis by krawtchouk momentsIEEE Transactions on Image Processing, 2003
- Exploring Recognition with Interchanged Facial FeaturesPerception, 1986