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Biometric Identification Using Visual System Classification on Handheld Devices

This overview discusses the potential of classifying users based on abnormalities and pathology in the visual system for biometric identification on handheld devices. It also presents a proposed experiment and highlights problems with the human visual system.

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Biometric Identification Using Visual System Classification on Handheld Devices

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  1. Biometric Identification Using Visual System Classification on Handheld Devices Robb Zucker

  2. Overview • The human eye and visual system • Classification potential based on visual system abnormalities and pathology • Proposed experiment

  3. Human Visual System

  4. Human Visual System

  5. Problems with the visual system • The National Institute for Health (NIH) lists as many as 15 common causes for decreased visual acuity • Some physical abnormalities to the visual system are inherent from birth (congenital) • Others, more commonly, occur with age (presbyopia)

  6. Light sensitivity • Light sensitivity decreases as we age, as early as age 20. The intensity of illumination for light to just be seen is doubled every 13 years thereafter • Due to: • resting diameter of pupil decreases (senile miosis) • Lens opacification • Vitreous humor opacification

  7. The dress… • Blue with black trim? • White with gold trim? Many factors effect how we see or perceive colors and shapes

  8. The Experiment • Create an app that forces users into difficult reading situations • Capture both the light sensitivity rating and the orientation for each user • Classification using K-Nearest Neighbor algorithm

  9. Device sensors and controls • INPUTS • Sensor.TYPE_LIGHT: Ambient light level in SI lux units • Sensor.TYPE_ORIENTATION: values[0]: Azimuth values[1]: Pitch, rotation around x-axis values[2]: Roll, rotation around the x-axis • OUTPUTS • Settings.System.SCREEN_BRIGHTNESS

  10. Effective Brightness Correct backlight brightness value for ambient light passively illuminating the device

  11. As effective brightness is increased, in what orientation are users holding the device?

  12. Expected results For a given brightness level person A person B person C Machine-Learning classification system based on k Nearest Neighbor (k-NN)

  13. Application • Additional security safeguard in a broader security system • As a compliment to challenge question • As a compliment to user defined security icon

  14. References • [1]Vision and Perception - Visual Processing • http://medicine.jrank.org/pages/1805/Vision-Perception-Visual-processing.html • [2] University of Calgary http://ucalgary.ca/pip369/mod9/aging/sensitivity • [3] Unar, J. A., Woo Chaw Seng, and Almas Abbasi. "A review of biometric technology along with trends and prospects." Pattern recognition 47.8 (2014): 2673-2688. • [4] News report via internet: • http://fox13now.com/2015/02/26/what-color-is-this-dress-viral-photo-stirs-intense-internet-debate/ • [5] Ross, Arun, and Anil Jain. Multimodal biometrics: An overview. na, 2004. • [6] National Institute of Health website: http://www.nlm.nih.gov/medlineplus/ency/article/003029.htm • [7] Ullah, Abrar, et al. "Graphical and text based challenge questions for secure and usable authentication in online examinations." Internet Technology and Secured Transactions (ICITST), 2014 9th International Conference for. IEEE, 2014.

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