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Toward practical mobile gait biometrics

Toward practical mobile gait biometrics

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Gait is the unique human locomotion due to individual specific biophysical and behavior habits. With ubiquitous mobile devices in people's daily life nowadays, accelerometers and gyroscopes provided in these devices directly capture the dynamic motion characteristics and thus have great potential for nonobtrusive gait biometrics. In fact, inertial sensors have been exploited to perform highly accurate gait analysis under controlled experimental settings. However, their performance in realistic scenarios is unsatisfactory due to variations in data measurements affected by physiological, environmental, and sensor-placement-related factors. Practical mobile gait biometric algorithms need to be robust to these variations to achieve high authentication performance in the field. It is the focus of this chapter to address some of these issues for in-the-wild mobile gait biometrics applications. First, we propose a novel gait representation called gait dynamics image (GDI) for accelerometer and gyroscope data sequences. GDIs are constructed to be both sensor-orientationinvariant and highly discriminative to enable high-performing gait biometrics for real-world applications. Second, we show how to further compute walking pacecompensated GDIs that are insensitive to variability in walking speed. Third, we adopt the i-vector paradigm, a state-of-the-art machine learning technique widely used for speaker recognition, to extract gait identities using the proposed invariant gait representation. Fourth, we demonstrate successful fusion of accelerometer and gyroscope modalities for improved authentication performance. Performance studies using both the naturalistic McGill University gait dataset and the large Osaka University gait dataset containing 744 subjects have shown dominant superiority of this novel gait biometrics approach compared to state-of-the-art. Additional performance evaluations on a realistic pace-varying mobile gait dataset containing 51 subjects confirm the merit of the proposed algorithm toward practical mobile gait authentication.

Chapter Contents:

  • Abstract
  • 10.1 Introduction
  • 10.2 Related work
  • 10.3 GDI gait representation
  • 10.3.1 Gait dynamics images
  • 10.3.1.1 GDI for accelerometer data
  • 10.3.1.2 Orientation invariants for gyroscope data
  • 10.3.1.3 Gait dynamics images
  • 10.3.1.4 Symmetry invariance properties of gait dynamics images
  • 10.3.2 Pace-compensated gait dynamics images
  • 10.3.2.1 Instantaneous gait cycle estimation using GDIs
  • 10.3.2.2 Energy function
  • 10.3.2.3 Efficient instantaneous gait cycle estimation using dynamic programming
  • 10.3.2.4 Pace-compensated gait dynamics images
  • 10.4 Gait identity extraction using i-vectors
  • 10.5 Performance analysis
  • 10.5.1 McGill University naturalistic gait dataset
  • 10.5.2 Osaka University largest gait dataset
  • 10.5.3 Mobile dataset with multiple walking speed
  • 10.6 Conclusions and future work
  • Acknowledgments
  • References

Inspec keywords: image sequences; sensor fusion; message authentication; gait analysis; vectors; image representation; mobile computing; learning (artificial intelligence)

Other keywords: ubiquitous mobile devices; gyroscope fusion; gait dynamics image; mobile gait biometrics; inertial sensors; accelerometer fusion; GDI; accelerometer data sequences; gyroscope data sequences; gait representation; authentication performance; machine learning; i-vector paradigm

Subjects: Ubiquitous and pervasive computing; Sensor fusion; Optical, image and video signal processing; Knowledge engineering techniques; Computer vision and image processing techniques; Data security

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