Paper Title :Face Liveness Detection Based on Features Fusion and Deep Learning Techniques
Author :Jitendra Chautharia, Prasanth Bodepudi
Article Citation :Jitendra Chautharia ,Prasanth Bodepudi ,
(2024 ) " Face Liveness Detection Based on Features Fusion and Deep Learning Techniques " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 75-80,
Volume-12,Issue-7
Abstract : Face liveness detection plays a crucial role in protecting facial recognition systems against spoofing attacks. In
this paper, we present a comparative analysis of face liveness detection techniques, focusing on the fusion of feature extraction
methods and the performance of traditional techniques versus deep learning models. We evaluate a range of feature extraction
methods, including Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Wavelet and edge
detection algorithms such as Step, Roberts, Sobel, and Laplacian. Additionally, we explore the effectiveness of combining
these feature extraction methods and apply the Support Vector Machine (SVM) classifier to differentiate between real faces
and spoofed images. Furthermore, we investigate the performance of pre-defined deep learning models, including
Convolutional Neural Networks (CNN), ResNet50, MobileNetV3, and Inception, in face liveness detection. Through
extensive experimentation on our dataset, we assess the strengths and limitations of each approach in terms of accuracy,
robustness, and computational efficiency. Our findings provide valuable insights into the effectiveness of different
techniques for face liveness detection and guiding the development of secure and reliable facial recognition systems.
Keywords - Image classification, Feature extraction, Deep learning, Traditional methods, Comparative analysis,
Convolutional Neural Networks, Support Vector Machine.
Type : Research paper
Published : Volume-12,Issue-7
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-21101
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Copyright: © Institute of Research and Journals
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Published on 2024-10-17 |
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