A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification
- Title
- A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification
- Creator
- Alphonse A.S.; Shankar K.; Jeyasheela Rakkini M.J.; Ananthakrishnan S.; Athisayamani S.; Robert Singh A.; Gobi R.
- Description
- In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK +), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and ManMachine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods. 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
- Source
- Journal of Ambient Intelligence and Humanized Computing, Vol-12, No. 3, pp. 3447-3463.
- Date
- 2021-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Classification; Emotion; Feature; Pattern; Texton
- Coverage
- Alphonse A.S., Department of Information Technology, Ponjesly College of Engineering, Nagercoil, India; Shankar K., Department of Computer Applications, Alagappa University, Karaikudi, India; Jeyasheela Rakkini M.J., School of Computing, Sastra Deemed To Be University, Thanjavur, India; Ananthakrishnan S., School of Computing, Sastra Deemed To Be University, Thanjavur, India; Athisayamani S., School of Computing, Sastra Deemed To Be University, Thanjavur, India; Robert Singh A., School of Computing, Kalasalingam Academy of Research and Education, Anand Nagar, India; Gobi R., Department of Computer Science, Christ University, Bengalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 18685137
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Alphonse A.S.; Shankar K.; Jeyasheela Rakkini M.J.; Ananthakrishnan S.; Athisayamani S.; Robert Singh A.; Gobi R., “A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/15888.