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by adham suwan mohammed zaza ahmed mafarjeh n.
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By : Adham Suwan Mohammed Zaza Ahmed Mafarjeh PowerPoint Presentation
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By : Adham Suwan Mohammed Zaza Ahmed Mafarjeh

By : Adham Suwan Mohammed Zaza Ahmed Mafarjeh

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By : Adham Suwan Mohammed Zaza Ahmed Mafarjeh

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  1. By : AdhamSuwan Mohammed Zaza Ahmed Mafarjeh

  2. Achieving Security through Kinect using Skeleton Analysis (ASKSA)

  3. Outline • Introduction • About the Kinect • Contribution • State Diagram & Software • Security Algorithm • Demo • Future Work • Conclusion

  4. ASKSA Project • Our project is a hardware / software solution that provides a more accurate security system. • Detects humans and differentiates the home owner from an ill-intentioned intruder. • Compares the skeleton dimensions of a person to those stored in the database. • Works well in all lighting situations specially in the dark. • Alerts the home owner in intrusion cases and reduces the false alarms as much as possible.

  5. Cont…

  6. About the Kinect • Motion sensing input device. • Invented by Microsoft. • Based around a webcam style. • Users interact with the Xbox360need no touch a game controller through a (NUI). • Not actually hacked, but someone wrote an open-source driver for PCs that essentially opens the USB connection and read the sensor inputs

  7. Cont… • The maximum depth range is 4 meters and the minimum range is 0.5 meter. • On 16-6-2011, Microsoft announced its official release of its SDK for non–commercial use.

  8. Kinect Components • RGB camera. • Tow 3D depth sensors. • Multi-array microphone that is capable of separate the voices that are in front of the device from the others sounds of the environment to use voice commands.

  9. Cont… • Use an infrared laser to project a matrix of dots and then the camera detects the distortion of each respective dot, enabling the Kinect to calculate the distance of each dot at 30 frames per second. • A depth matrix produces, which is the distance of each pixel.

  10. Kinect Features • Developers use the Kinect to build interesting applications C++, C# or VB.NET. • Raw sensor stream: access to low-level streams from the depth sensor. • Skeletal tracking: track the skeleton image of a person moving within the Kinect field. • Advance audio capabilities: supports the voice recognition technique.

  11. Contribution • Biometric Authentication: ASKSA to automatically differentiate between a known person and an unknown person based on skeletal recognition technology. • Enhancement of security cameras’ utility and minimization of false alarms. • ASKSA works in various lighting conditions and in the dark. • ASKSA security algorithm is an efficient lookup against an in-memory biometric database. • ASKSA is inexpensive compared with current security solutions.

  12. State Diagram

  13. Software Modules • Kinect Manager • Alarm Manager • Authentication Manager • Mail Manager • Twillio Manager

  14. Security Algorithm • Kinect provides a collection of 20 joint positions, each with an x, y, and z • Our system relies on the distances between the joints of the skeleton of the human • 9 distances are taken into account (12 joints) • All the 12 joints must be detected when authenticating, undetected joint get ∞ position • It is almost impossible to match the 9 distances between two different persons  Secure Algorithm 

  15. Cont… • In order for a seen person to pass authentication, the sum of the differences of the lengths from a known person must be within a specific threshold

  16. Authentication Process • Measurements for the seen person joints are taken • ASKSA starts searching for the closest known person in the database • If the sum of the differences of the lengths for the seen person from a known person is less than 12 cm the authentication success, otherwise the authentication fails • ASKSA give the person 10 seconds to pass/fail the authentication • What if the person get close/far from the Kinect ?!

  17. Equations Used • delta: total differences • epsilon: max allow delta • So, if delta < epsilon authentication successful ! • delta = • Where • d1 is the distance between 2 joints of the detected person • d2 is the distance between 2 joints of the known person • Euclidian distance

  18. Numerical Notes • The average lengths for the 9 segments we took for ordinary people is approximately 327 cm • 12/327 = 3.7%  ASKSA is able to differentiate two persons who have at least 3.7% difference in their skeletal dimensions • 12 cm was taken as threshold after several experiments on many persons • Kinect operates @ 30 FPS  10 sec = 300 frame • If delta < epsilonin one of the 300 frames the authentication immediately success • Any ∞ position joint make the authentication fails

  19. Snapshots & Demo

  20. Future Work • Adding a new skeleton to the known person database via voice commands. • Adding a filter to smooth the fluctuations of Kinect’s measurements , which would prevent false matches. • Adding face recognition and more skeleton joints

  21. Conclusion • Skeletal recognition technology is predicted have a bright future. • Play Station 4 uses a built-in Kinect that uses facial recognition and skeletal dimensions to differentiate players. • ASKSA provides a security system, which has fewer false alarms, is more secure, and inexpensive compared with current security solutions.