Regional adaptive affinitive patterns (RADAP) with logical operators for facial expression recognition
| Autor: | Sonakshi Mathur, Santosh Kumar Vipparthi, Monu Verma, Subrahmanyam Murala, Kranthi Kumar Deveerasetty, Murari Mandal |
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| Rok vydání: | 2019 |
| Předmět: |
Facial expression
Adder Contextual image classification business.industry Computer science Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology Facial recognition system Facial expression recognition Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pairwise comparison Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering Invariant (mathematics) business Software |
| Zdroj: | IET Image Processing. 13:850-861 |
| ISSN: | 1751-9667 |
| DOI: | 10.1049/iet-ipr.2018.5683 |
| Popis: | Automated facial expression recognition plays a significant role in the study of human behaviour analysis. In this study, the authors propose a robust feature descriptor named regional adaptive affinitive patterns (RADAP) for facial expression recognition. The RADAP computes positional adaptive thresholds in the local neighbourhood and encodes multi-distance magnitude features which are robust to intra-class variations and irregular illumination variation in an image. Furthermore, they established cross-distance co-occurrence relations in RADAP by using logical operators. They proposed XRADAP, ARADAP, and DRADAP using xor, adder and decoder, respectively. The XRADAP engrains the quality of robustness to intra-class variations in RADAP features using pairwise co-occurrence. Similarly, ARADAP and DRADAP extract more stable and illumination invariant features and capture the minute expression features which are usually missed by regular descriptors. The performance of the proposed methods is evaluated by conducting experiments on nine benchmark datasets Cohn-Kanade+ (CK+), Japanese female facial expression (JAFFE), Multimedia Understanding Group (MUG), MMI, OULU-CASIA, Indian spontaneous expression database, DISFA, AFEW and Combined (CK+, JAFFE, MUG, MMI & GEMEP-FERA) database in both person dependent and person independent setup. The experimental results demonstrate the effectiveness of the proposed method over state-of-the-art approaches. |
| Databáze: | OpenAIRE |
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