Active authentication using facial attributes

Active authentication using facial attributes

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The focus of this chapter is on face-based biometric authentication methods. These methods first use the camera sensor images to detect the face of the users. Next, they extract low-level features from the face images, and then apply their algorithm to the extracted features to authenticate the user. These algorithms have access to some model of the enrolled user for comparison. In [11], Hadid et al. use Haar cascade and Adaboost of [12] to detect face components and then use Local Binary Pattern (LBP) histograms [13] and nearest-neighbor thresholding for authentication. In [7], Fathy et al. extract two types of intensity features from the full face and facial parts and compare four still image-based verification algorithms with four image set-based methods. The common pitfall of most of these algorithms is that they are very sensitive to changes in the low-level feature domain. They are sensitive in the sense that if two face images are under the same pose and lighting condition, they can perform well, but in unconstrained settings they become very inaccurate.

Chapter Contents:

  • 5.1 Introduction
  • 5.2 Facial attribute classifiers
  • 5.2.1 Linear attribute classifiers
  • 5.2.2 Convolutional neural network attribute model
  • Overfitting
  • Hyperparameters
  • 5.2.3 Performance of the attribute classifiers
  • 5.3 Authentication
  • 5.3.1 Short-term authentication
  • 5.3.2 Long-term authentication
  • 5.3.3 Discussion
  • 5.4 Platform implementation feasibility
  • 5.4.1 Memory
  • 5.4.2 Computation efficiency and power consumption
  • 5.5 Summary and discussion
  • Acknowledgments
  • References

Inspec keywords: message authentication; feature extraction; image segmentation; authorisation; biometrics (access control); Haar transforms; face recognition

Other keywords: LBP; active authentication; facial attributes; face images; Adaboost; low-level feature extraction; local binary pattern; nearest-neighbor thresholding; biometric authentication; Haar cascade

Subjects: Computer vision and image processing techniques; Data security; Image recognition; Integral transforms; Integral transforms

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