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Rajan Patel, Jaynish Patel, Nikul Patel, Jinfeng Jiang, Advisor: Prof. Philip Southard Goal ❑ Connect the USB Webcam, the HDMI Output, and the Python OpenCV Facial Recognition system to the PYNQ Z1 Board. ❑ Read the frame from the USB webcam and make a grayscale working copy of the image that is scaled down for faster processing. ❑ Detect the face in the copy using Python OpenCV. ❑ Compare the detected face in the copy to the user faces in the database. Recognize the detected face if the user is found in the database and update the log. ❑ Draw rectangles and text on the full scale color image ❑ Output the modified image to HDMI ❑ Unlock Entry System if user is recognized ❑ Design an access control system, utilizing facial recognition as its method of authentication. ❑ Store authorized users and entry attempts (with timestamps and identifying images) in a SQLite database. ❑ Enhance real-time capabilities and increase processing speed to reduce latency in identification, via FPGA acceleration. Methodology Results References [1] https://pynq.readthedocs.io/en/v2.4 [2] https://github.com/Xilinx/PYNQ-HelloWorld [3] https://groups.google.com/forum/#!forum/pynq_project We would like to thank our advisor Prof. Phillip Southard for his guidance and continued support. The FPGA Accelerated Controlled Entry System (FACES) Research Challenges ❑ Compare methods of Facial Recognition ❑ Optimizing Speed vs. Quality ❑ Identify bottleneck and replace portions of the OpenCV with hardware implementations. Motivations ❑ Modern technology is moving away from traditional password based authentication in favor of biometric identification. ❑ Facial recognition is the superior method of biometric identification in terms of efficiency. ❑ Very few physical control entry systems that rely on facial recognition exist. ❑ The system is able to detect and recognize faces with acceptable speed ❑ The latch is unlocked only for faces with a high enough access level ❑ Portions of the image processing code have been moved into the FPGA fabric Future Goals/Plans ❑ Use industrial scale equipment to improve camera and locking mechanism quality ❑ Applicable to businesses where access restriction is necessary ❑ Increase dataset to increase the accuracy of the facial recognition system USB Webcam Python/OpenCV PYNQ Z1 HDMI OUTPUT ENTRY SYSTEM USER DATABASE

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Page 1: The FPGA Accelerated Controlled Entry System (FACES)

Rajan Patel, Jaynish Patel,Nikul Patel, Jinfeng Jiang,

Advisor: Prof. Philip Southard

Goal

❑ Connect the USB Webcam, the HDMI Output, and the Python OpenCV Facial Recognition system to the PYNQ Z1 Board.

❑ Read the frame from the USB webcam and make a grayscale working copy of the image that is scaled down for faster processing.

❑ Detect the face in the copy using Python OpenCV.

❑ Compare the detected face in the copy to the user faces in the database. Recognize the detected face if the user is found in the database and update the log.

❑ Draw rectangles and text on the full scale color image

❑ Output the modified image to HDMI

❑ Unlock Entry System if user is recognized

❑ Design an access control system, utilizing facial recognition as its method of authentication.

❑ Store authorized users and entry attempts (with timestamps and identifying images) in a SQLite database.

❑ Enhance real-time capabilities and increase processing speed to reduce latency in identification, via FPGA acceleration.

Methodology

Results

References[1] https://pynq.readthedocs.io/en/v2.4[2] https://github.com/Xilinx/PYNQ-HelloWorld[3] https://groups.google.com/forum/#!forum/pynq_project

We would like to thank our advisor Prof. Phillip Southard for his guidance and continued support.

The FPGA Accelerated Controlled Entry System (FACES)

Research Challenges

❑ Compare methods of Facial Recognition

❑ Optimizing Speed vs. Quality

❑ Identify bottleneck and replace portions of the OpenCV with hardware implementations.

Motivations

❑ Modern technology is moving away from traditional password based authentication in favor of biometric identification.

❑ Facial recognition is the superior method of biometric identification in terms of efficiency.

❑ Very few physical control entry systems that rely on facial recognition exist.

❑ The system is able to detect and recognize faces with acceptable speed

❑ The latch is unlocked only for faces with a high enough access level

❑ Portions of the image processing codehave been moved into the FPGA fabric

Future Goals/Plans

❑ Use industrial scale equipment to improve camera and locking mechanism quality

❑ Applicable to businesses where access restriction is necessary

❑ Increase dataset to increase the accuracy of the facial recognition system

USB Webcam

Python/OpenCVPYNQ Z1

HDMI OUTPUT ENTRY SYSTEM

USER

DATABASE

https://pynq.readthedocs.io/en/v2.4
https://github.com/Xilinx/PYNQ-HelloWorld
https://groups.google.com/forum/#!forum/pynq_project

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