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Prosopagnosia01:24

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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American Sign Language Alphabet Recognition Using a Neuromorphic Sensor and an Artificial Neural Network.

Miguel Rivera-Acosta1, Susana Ortega-Cisneros2, Jorge Rivera3

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This study presents a hardware-based American Sign Language (ASL) alphabet recognition system using a Field-Programmable Gate Array and a neuromorphic camera. The system achieved 79.58% accuracy in classifying ASL signs.

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Area of Science:

  • Computer Engineering
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • American Sign Language (ASL) recognition is crucial for human-computer interaction.
  • Neuromorphic cameras (Dynamic Vision Sensors, DVS) offer event-based data processing advantages.
  • Hardware implementation is key for real-time, high-speed sign language translation.

Purpose of the Study:

  • To design and analyze an ASL alphabet translation system implemented in hardware.
  • To develop efficient digital image processing algorithms for DVS data.
  • To achieve high-speed ASL classification using an artificial neural network on a reconfigurable device.

Main Methods:

  • Utilized a neuromorphic camera (DVS) for data acquisition via USB.
  • Developed software-based algorithms for feature extraction from DVS events.
  • Implemented a single artificial neural network in digital hardware for ASL sign classification.

Main Results:

  • A complete ASL sign contour classification system was developed on a Field-Programmable Gate Array.
  • Experimental results with 720 samples of 24 signs yielded a recognition accuracy of 79.58%.
  • Demonstrated the effectiveness of digital image processing algorithms for DVS data.

Conclusions:

  • The hardware-implemented ASL translation system shows promising results for real-time applications.
  • The integration of neuromorphic sensing and FPGA-based processing enables efficient sign language recognition.
  • Further research can optimize algorithms and network architecture for improved accuracy.