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Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using

Dat Tien Nguyen1, Tuyen Danh Pham2, Na Rae Baek3

  • 1Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea. nguyentiendat@dongguk.edu.

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Summary

This study introduces a novel presentation attack detection (PAD) method for face recognition systems. Combining deep learning features from convolutional neural networks (CNNs) with multi-level local binary pattern (MLBP) skin details significantly improves fake face detection accuracy.

Keywords:
convolutional neural networkface recognitionmulti-level local binary patternpresentation attack detectionvisible-light camera sensor

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Area of Science:

  • Computer Vision
  • Biometrics
  • Artificial Intelligence

Background:

  • Face recognition systems are vulnerable to presentation attacks (fake samples).
  • Existing presentation attack detection (PAD) methods using handcrafted features (e.g., LBP, HOG) have limited accuracy.
  • Deep learning offers automatic feature extraction capabilities to enhance PAD performance.

Purpose of the Study:

  • To develop a robust PAD method overcoming limitations of traditional approaches.
  • To improve the security of face recognition systems against presentation attacks.
  • To enhance feature discrimination by combining deep and handcrafted features.

Main Methods:

  • Utilized Convolutional Neural Network (CNN) for deep feature extraction.
  • Employed Multi-Level Local Binary Pattern (MLBP) for skin detail feature extraction.
  • Integrated deep and MLBP features into a hybrid feature set.
  • Classified hybrid features using Support Vector Machine (SVM).

Main Results:

  • The proposed hybrid feature method demonstrated superior discrimination ability compared to single feature types.
  • Experimental results showed significantly lower error rates than previous PAD methods on standard databases.
  • The combination of deep and handcrafted features effectively distinguished real from fake face images.

Conclusions:

  • The proposed hybrid feature-based PAD method offers enhanced security for face recognition systems.
  • Combining CNN-derived deep features with MLBP skin details is effective for presentation attack detection.
  • This approach represents a significant advancement in reliable face recognition technology.