Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Face recognition by elastic bunch graph matching
Intelligent biometric techniques in fingerprint and face recognition
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes
International Journal of Computer Vision
EMPATH: A Neural Network that Categorizes Facial Expressions
Journal of Cognitive Neuroscience
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
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The human perceptual system performs rapid processing within the early visual system: low spatial frequency information is processed rapidly through magnocellular layers, whereas the parvocellular layers process all the spatial frequencies more slowly. The purpose of the present paper is to test the usefulness of low spatial frequency (LSF) information compared to high spatial frequency (HSF) and broad spatial frequency (BSF) visual stimuli in a classification task of emotional facial expressions (EFE) by artificial neural networks. The connectionist modeling results show that an LSF information provided by the frequency domain is sufficient for a distributed neural network to correctly classify EFE, even when all the spatial information relating to these images is discarded. These results suggest that the HSF signal, which is also present in BSF faces, acts as a source of noisy information for classification tasks in an artificial neural system.