IEEE Transactions on Pattern Analysis and Machine Intelligence
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Automatic Generation of Cognitive Theories using Genetic Programming
Minds and Machines
A New Associative Model with Dynamical Synapses
Neural Processing Letters
The Role of the Infant Vision System in 3D Object Recognition
Advances in Neuro-Information Processing
Design of artificial neural networks using a modified particle swarm optimization algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
3D object recognition based on low frequency response and random feature selection
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Low frequency response and random feature selection applied to face recognition
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
| Hi-index | 0.00 |
Visual attention is a powerful mechanism that enables perception to focus on a small subset of the information picked up by our eyes It is directly related to the accuracy of an object categorization task In this paper we adopt those biological hypotheses and propose an evolutionary visual attention model applied to the face recognition problem The model is composed by three levels: the attentive level that determines where to look by means of a retinal ganglion network simulated using a network of bi-stable neurons and controlled by an evolutionary process; the preprocessing level that analyses and process the information from the retinal ganglion network; and the associative level that uses a neural network to associate the visual stimuli with the face of a particular person To test the accuracy of the model a benchmark of faces is used.