Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Mathematical Psychology
Distance measures for PCA-based face recognition
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Neural Computation
Associative Memories Applied to Pattern Recognition
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Hetero-Associative Memories for Voice Signal and Image Processing
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Voice Translator Based on Associative Memories
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
A Bidirectional Hetero-Associative Memory for True-Color Patterns
Neural Processing Letters
A New Associative Model with Dynamical Synapses
Neural Processing Letters
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
An evolutionary feature-based visual attention model applied to face recognition
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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A novel method for face recognition based on some biological aspects of infant vision is proposed in this paper. The biological hypotheses of this method are based on the role of the response to low frequencies at early stages, and some conjectures concerning how an infant detects subtle features (stimulating points) from a face. In order to recognize a face from different images of it we make use of a bank of dynamic associative memories (DAM). As the infant vision responds to low frequencies of the signal, a low-filter is first used to remove high frequency components from the image. We then detect subtle features in the image by means of a random feature selection detector. At last, the network of DAMs is fed with this information for training and recognition. To test the accuracy of the proposal a benchmark of faces is used.