Gammadion binary pattern of Shearlet coefficients (GBPSC): An illumination-invariant heterogeneous face descriptor
| Autor: | Subhadeep Koley, Hiranmoy Roy, Debotosh Bhattacharjee |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Local binary patterns business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Binary pattern 01 natural sciences Facial recognition system Convolutional neural network Artificial Intelligence Feature (computer vision) Shearlet 0103 physical sciences Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Noise (video) Artificial intelligence Invariant (mathematics) 010306 general physics business Software |
| Zdroj: | Pattern Recognition Letters. 145:30-36 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2021.01.028 |
| Popis: | This paper presents a novel face image descriptor called Gammadion Binary Pattern of Shearlet Coefficients (GBPSC) for illumination and noise invariant, homogeneous and heterogeneous face recognition. Exploiting the energy concentration property of the Digital Shearlet Transform, an efficient illumination and noise invariant feature extractor has been devised. Finally, inspired by the Gammadion structure, a robust multi-directional local binary pattern named Gammadion Binary Pattern (GBP) has been proposed. GBP is applied on the previously extracted illumination and noise invariant feature map to generate the GBPSC images. Recognition results on Extended Yale B and TUFTS dataset indicate the primacy of the proposed scheme in terms of common feature representation under varying illumination, and modality. Furthermore, the merger of the proposed GBPSC and Convolutional Neural Network (CNN) consistently outperforms other state-of-the art methods. |
| Databáze: | OpenAIRE |
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