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The Classification Technique for Face Spoof Detection in Artificial Neural Networks

  • Unique Paper ID: 172481
  • Volume: 11
  • Issue: 8
  • PageNo: 3316-3323
  • Abstract:
  • Face spoof detection is a vital component of biometric security systems, designed to protect against malicious attacks such as presentation attacks. With the rapid advancements in deep learning, particularly the use of Artificial Neural Networks (ANNs), face spoof detection has undergone significant improvements. This paper presents an in-depth review of classification techniques for face spoof detection utilizing ANNs. It explores various architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, while also delving into key aspects like datasets, preprocessing methods, and evaluation metrics. The findings indicate that ANN-based classifiers, particularly CNNs, excel in detecting spoofing attempts by efficiently learning discriminative features from facial images, establishing them as effective tools for securing biometric authentication systems.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 8
  • PageNo: 3316-3323

The Classification Technique for Face Spoof Detection in Artificial Neural Networks

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