1
|
Sultan Zia M, Hussain M, Arfan Jaffar M. Incremental Learning-Based Facial Expression Classification System Using a Novel Multinomial Classifier. INT J PATTERN RECOGN 2017. [DOI: 10.1142/s0218001418560049] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Facial expressions recognition is a crucial task in pattern recognition and it becomes even crucial when cross-cultural emotions are encountered. Various studies in the past have shown that all the facial expressions are not innate and universal, but many of them are learned and culture-dependent. Extreme facial expression recognition methods employ different datasets for training and later use it for testing and demostrate high accuracy in recognition. Their performances degrade drastically when expression images are taken from different cultures. Moreover, there are many existing facial expression patterns which cannot be generated and used as training data in single training session. A facial expression recognition system can maintain its high accuracy and robustness globally and for a longer period if the system possesses the ability to learn incrementally. We also propose a novel classification algorithm for multinomial classification problems. It is an efficient classifier and can be a good choice for base classifier in real-time applications. We propose a facial expression recognition system that can learn incrementally. We use Local Binary Pattern (LBP) features to represent the expression space. The performance of the system is tested on static images from six different databases containing expressions from various cultures. The experiments using the incremental learning classification demonstrate promising results.
Collapse
Affiliation(s)
- M. Sultan Zia
- COMSATS, Institute of Information Technology, Sahiwal, Pakistan
| | - Majid Hussain
- COMSATS, Institute of Information Technology, Sahiwal, Pakistan
- Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - M. Arfan Jaffar
- Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| |
Collapse
|
2
|
Fairhurst M, Li C, Da Costa‐Abreu M. Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data. IET BIOMETRICS 2017. [DOI: 10.1049/iet-bmt.2016.0169] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Michael Fairhurst
- School of Engineering and Digital ArtsUniversity of KentCanterburyKent CT2 7NTUK
| | - Cheng Li
- School of Engineering and Digital ArtsUniversity of KentCanterburyKent CT2 7NTUK
| | | |
Collapse
|
3
|
Fairhurst M, Erbilek M, Li C. Study of automatic prediction of emotion from handwriting samples. IET BIOMETRICS 2015. [DOI: 10.1049/iet-bmt.2014.0097] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Michael Fairhurst
- School of Engineering and Digital ArtsUniversity of KentCanterburyKentCT2 7NTUK
| | - Meryem Erbilek
- School of Engineering and Digital ArtsUniversity of KentCanterburyKentCT2 7NTUK
| | - Cheng Li
- School of Engineering and Digital ArtsUniversity of KentCanterburyKentCT2 7NTUK
| |
Collapse
|