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Face Anti-spoofing Algorithm Based on Gray Level Co-occurrence Matrix and Dual Tree Complex Wavelet Transform
- Source :
- Lecture Notes in Computer Science ISBN: 9783319689340, IDEAL
- Publication Year :
- 2017
- Publisher :
- Springer International Publishing, 2017.
-
Abstract
- By analyzing the difference of facial texture features between living face and photo, we propose a novel face anti-spoofing algorithm based on gray level co-occurrence matrix (GLCM) and dual-tree complex wavelet tree (DT-CWT). Firstly, inspired by the co-occurrence matrix, we extract five texture features including angle second moment, entropy, contrast, correlation and local uniformity to represent the gray direction, interval and amplitude information for the face texture information. Secondly, DT-CWT has the advantages of approximate translation invariance and good direction selectivity. Therefore, the coefficients of DT-CWT can enhance the texture information and edge information in the frequency domain. At last, the SVM classification is used to distinguish between true and fake face. Our algorithm is demonstrated on the published NUAA database. Compared with the existing methods, the feature dimension is reduced. The experimental results show that the proposed algorithm improves the detection accuracy.
- Subjects :
- Discrete wavelet transform
021110 strategic, defence & security studies
Computer science
business.industry
Stationary wavelet transform
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
0211 other engineering and technologies
Wavelet transform
Cascade algorithm
Pattern recognition
02 engineering and technology
Wavelet packet decomposition
Co-occurrence matrix
Wavelet
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
Complex wavelet transform
business
Algorithm
Subjects
Details
- ISBN :
- 978-3-319-68934-0
- ISBNs :
- 9783319689340
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783319689340, IDEAL
- Accession number :
- edsair.doi...........1eab61aebbf0f9d4f208a90a17c3ab83