Recent advances in unconstrained face recognition

Recent advances in unconstrained face recognition

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Many face recognition systems have demonstrated promising results under well-controlled conditions with cooperative users. However, face recognition in real-world scenarios is still a challenging problem due to dramatic facial variations caused by different poses, lighting conditions, expressions, occlusion and so on. In this chapter, we summarize recent advances in unconstrained face recognition. We begin by introducing existing unconstrained face databases or benchmarks. We then provide an overview of recent techniques specifically developed for this task, including advanced face representations, metric learning approaches, background information investigation and pose-invariant approaches. Finally, we highlight some open issues to be addressed.

Chapter Contents:

  • 8.1 Introduction
  • 8.2 Real-world databases
  • 8.2.1 LFW benchmark
  • 8.2.2 PubFig database
  • 8.2.3 YTF video database
  • 8.2.4 Point-and-shoot face recognition challenge
  • 8.2.5 MegaFace dataset
  • 8.2.6 IJB-A dataset
  • 8.3 Face representations
  • 8.3.1 Local appearance features
  • 8.3.2 Descriptors learned by encoding local microstructures
  • 8.3.3 Aggregation of local appearance features
  • 8.3.4 Features learned by deep neural networks
  • 8.4 Metric learning approaches
  • 8.5 Background information investigation
  • 8.6 Pose-invariant face recognition
  • 8.7 Performance evaluation
  • 8.8 Open issues
  • 8.8.1 Large-scale face recognition in real-world security scenarios
  • 8.8.2 Pose-invariant face recognition
  • 8.8.3 Age-invariant face recognition
  • 8.8.4 Dependence on large amount of labeled training data
  • Acknowledgments
  • References

Inspec keywords: learning (artificial intelligence); face recognition

Other keywords: unconstrained face recognition; face recognition systems; advanced face representations

Subjects: Image recognition; Computer vision and image processing techniques

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