Face Similarity Space as Perceived by Humans and Artificial Systems
نویسندگان
چکیده
The performance of a local feature based system, using Gabor-filters, and a global template matching based system, using a combination of PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), was correlated with human performance on a recognition task involving 32 face images. Both systems showed qualitative similarities to human performance in that all but one of the calculated correlation coefficients were very or moderately high. The Gabor filter model seemed to capture human performance better than the PCA-LDA model since the coefficients for this model were higher for all examined conditions. Analysis of additional systems based on only PCA, only LDA, and ICA (Independent Component Analysis) is currently in progress.
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