A Novel Fingerprint Liveness Detection Method using Empirical Mode Decomposition and Neural Network

One of the most common biometric systems is fingerprint identification, which has been misused due to issues such as fraud. Hence, intelligent methods should be designed and used to recognize real-live fingerprints. Therefore, in the current work, we proposed a novel liveness fingerprint detection framework with low computational cost and excellent accuracy based on empirical mode decomposition and neural network to distinguish real from fake fingerprints. Our proposed scheme works based on empirical mode decomposition technique. The fingerprint images were cropped into 200 × 200 images and then the two-dimensional (2D) images were converted into one-dimensional (1D) data, greatly reducing the computational process. The empirical mode decomposition (EMD) technique decomposed the data and the first five intrinsic mode functions (IMFs) were targeted for feature extraction through simple statistical features. The findings revealed that our suggested system can yield an average accuracy of 97.72% in distinguishing fake from real fingerprints through multilayer perceptron (MLP) neural network. This framework is very efficient compared to other techniques because only one piece of fingerprint image is enough to defend against spoof attacks. Therefore, such framework can reduce the cost of the fingerprint biometric systems, as no further hardware is needed. In addition, our framework method gives the best classification results in comparison to other previous techniques in real-live fingerprint recognition while being simple with lower computational cost. Therefore, this framework can be practically used in commercial biometric systems.

@article{Tong2023,
  title     = {A Novel Fingerprint Liveness Detection Method using Empirical Mode Decomposition and Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Shekun Tong and Chunmeng Lu},
  doi       = {10.14569/IJACSA.2023.01409117},
  url       = {https://doi.org/10.14569/IJACSA.2023.01409117}
}