Is this project an undergraduate, graduate, or faculty project?

Undergraduate

individual

Authors' Class Standing

Daniel Bussey, Senior Alex Glandon, Graduate Student Lasitha Vidyaratne, Graduate Student Mahbubul Alam, Graduate Student Khan Iftekharuddin, Faculty

Lead Presenter's Name

Daniel Bussey

Faculty Mentor Name

Khan Iftekharuddin (ODU)

Abstract

Applications of transfer learning for convolutional neural networks (CNNs) have shown to be an efficient alternative for solving recognition tasks rather than designing and training a new neural network from scratch. However, there exists several popular CNN architectures available for various recognition tasks. Therefore, choosing an appropriate network for a specific recognition task, specifically designed for a humanoid robotic platform, is often challenging. This study evaluates the performance of two well-known CNN architectures; AlexNet, and VGG-Face for a face recognition task. This is accomplished by applying the transfer learning concept to the networks pre-trained for different recognition tasks. The proposed face recognition framework is then implemented on a humanoid robot known as NAO to demonstrate the practicality and flexibility of the algorithm. The results suggest that the proposed pipeline shows excellent performance in recognizing a new person from a single example image under varying distance and resolution conditions usually applicable to a mobile humanoid robotic platform.

Did this research project receive funding support (Spark or Ignite Grants) from the Office of Undergraduate Research?

No

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Convolutional Neural Network Transfer Learning for Robust Face Recognition in NAO Humanoid Robot

Applications of transfer learning for convolutional neural networks (CNNs) have shown to be an efficient alternative for solving recognition tasks rather than designing and training a new neural network from scratch. However, there exists several popular CNN architectures available for various recognition tasks. Therefore, choosing an appropriate network for a specific recognition task, specifically designed for a humanoid robotic platform, is often challenging. This study evaluates the performance of two well-known CNN architectures; AlexNet, and VGG-Face for a face recognition task. This is accomplished by applying the transfer learning concept to the networks pre-trained for different recognition tasks. The proposed face recognition framework is then implemented on a humanoid robot known as NAO to demonstrate the practicality and flexibility of the algorithm. The results suggest that the proposed pipeline shows excellent performance in recognizing a new person from a single example image under varying distance and resolution conditions usually applicable to a mobile humanoid robotic platform.

 

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