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Constructive autoassociative neural network for facial recognition.

Bruno J T Fernandes1, George D C Cavalcanti2, Tsang I Ren2

  • 1Escola Politécnica, Universidade de Pernambuco, Recife-PE, Brazil.

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|December 27, 2014
PubMed
Summary
This summary is machine-generated.

We introduce CANet, a novel Constructive Autoassociative Neural Network (CANet) for computer vision. This dynamic neural network architecture improves face recognition and facial expression recognition performance compared to existing methods.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Neural Networks

Background:

  • Defining optimal neural network architectures for computer vision is challenging due to domain-specific requirements and reliance on prior knowledge.
  • Existing autoassociative neural networks often lack adaptability in their architecture.

Purpose of the Study:

  • To propose a novel, adaptable neural network architecture for computer vision tasks.
  • To address the limitations of fixed neural network designs in autoassociative applications.

Main Methods:

  • Developed CANet (Constructive Autoassociative Neural Network), a dynamic architecture integrating receptive fields and autoassociative memory.
  • Implemented a constructive approach by adding neurons to the hidden layer and a pruning algorithm for the output layer.
  • Incorporated lateral inhibitory connections in the output layer neurons to enhance recognition.

Main Results:

  • CANet demonstrated superior performance in face recognition tasks.
  • Experiments confirmed CANet's effectiveness in facial expression recognition.
  • The dynamic architecture and inhibitory connections contributed to improved recognition rates.

Conclusions:

  • CANet offers an adaptive and effective solution for autoassociative neural network applications in computer vision.
  • The proposed architecture outperforms existing methods in benchmark face and facial expression recognition tasks.
  • CANet's dynamic nature and specific neuron configurations are key to its enhanced performance.