Face Anti-Spoofing Using Chainlets and Deep Learning

Now-a-days, biometric technology is widely employed for many security purposes. Facial recognition is one of the biometric technologies that is increasingly utilized because it is convenient and contactless. However, the facial recognition system has become the most targeted by unauthorized users to get access to the system. Most facial recognition systems are vulnerable to face spoofing attacks. With the widespread use of the internet and social media, it has become easy to get videos or pictures of people’s faces. The imposter can use these documents to deceive facial authentication systems, which affects the system’s security and privacy. Face spoofing occurs when an unauthorized user attempts to gain access to a facial recognition system using presentation attack instruments (PAIs) such as photos, videos, or 3D masks of the authorized users. Therefore, the need for an effective face anti-spoofing (FAS) system is increased. That motivated us to develop a face anti-spoofing model that accurately detects presentation attacks. In our work, we developed a model that integrates handcrafted features based on Chainlets (as motion-based descriptor) and the convolutional neural network (CNN) to provide a more robust feature vector and enhance accuracy performance. Chainlets can be computed from deep contour-based edge detection using Histograms of Freeman Chain Codes, which provides a richer and rotation-invariant description of edge orientation that can be used to extract Chainlets features. We used a benchmark dataset, the Replay-Attack database. The result shows that the Chainlets-based face anti-spoofing method overcome the state-of-art methods and provide higher accuracy.

@article{Alrethea2024,
  title     = {Face Anti-Spoofing Using Chainlets and Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Sarah Abdulaziz Alrethea and Adil Ahmad},
  doi       = {10.14569/IJACSA.2024.0151180},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151180}
}