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Face Recognition on a Smart Image Sensor Using Local Gradients.

Wladimir Valenzuela1, Javier E Soto1, Payman Zarkesh-Ha2

  • 1Department of Electrical Engineering, Universidad de Concepción, Concepción 4070386, Chile.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a smart imaging sensor (SIS) for efficient face recognition. It utilizes custom smart pixels and ringed local binary patterns (RLBP) for high-accuracy, low-power facial image classification.

Keywords:
face recognitionfeature extractionfield-programmable gate arrayintelligent sensorlinear binary patternslinear discriminant analysissmart image sensorsmart pixelvery large-scale integrationvision chip

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Area of Science:

  • Computer Engineering
  • Image Processing
  • Biometric Security

Background:

  • Face recognition systems require efficient feature extraction and classification.
  • Traditional methods often involve high computational costs and power consumption.
  • Analog domain processing offers potential for reduced power usage in imaging sensors.

Purpose of the Study:

  • To present the architecture of a novel smart imaging sensor (SIS) for face recognition.
  • To develop a custom smart pixel for analog computation of spatial gradients.
  • To implement an efficient face recognition algorithm using these hardware components.

Main Methods:

  • Designed a smart pixel capable of analog computation of local spatial gradients.
  • Developed a lightweight local feature descriptor termed ringed local binary patterns (RLBP).
  • Implemented a three-stage face recognition algorithm: RLBP feature extraction, histogram computation, and nearest neighbor classification in a projected subspace.

Main Results:

  • The smart pixel was designed using TSMC 0.35 μm mixed-signal CMOS process, achieving high fill factors on different processes.
  • The pixel array operates at high frame rates up to 556 frames per second.
  • The digital coprocessor achieved 96.5% classification accuracy on infrared face images, with rapid classification (94 μs) and low power consumption (71 mW).

Conclusions:

  • The proposed SIS architecture enables efficient and accurate face recognition.
  • Analog computation of spatial gradients in smart pixels significantly reduces processing load.
  • The system demonstrates a viable approach for low-power, high-speed biometric applications.