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On the Dimensionality of Face Space
July 2007 (vol. 29 no. 7)
pp. 1262-1267
| ASCII Text | x | ||
| Marsha Meytlis, Lawrence Sirovich, "On the Dimensionality of Face Space," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 7, pp. 1262-1267, July, 2007. | |||
| BibTex | x | ||
| @article{ 10.1109/TPAMI.2007.1033, author = {Marsha Meytlis and Lawrence Sirovich}, title = {On the Dimensionality of Face Space}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {29}, number = {7}, issn = {0162-8828}, year = {2007}, pages = {1262-1267}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.1033}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - On the Dimensionality of Face Space IS - 7 SN - 0162-8828 SP1262 EP1267 EPD - 1262-1267 A1 - Marsha Meytlis, A1 - Lawrence Sirovich, PY - 2007 KW - Face and gesture recognition KW - computational models of vision KW - psychology KW - singular value decomposition. VL - 29 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
The dimensionality of face space is measured objectively in a psychophysical study. Within this framework, we obtain a measurement of the dimension for the human visual system. Using an eigenface basis, evidence is presented that talented human observers are able to identify familiar faces that lie in a space of roughly 100 dimensions and the average observer requires a space of between 100 and 200 dimensions. This is below most current estimates. It is further argued that these estimates give an upper bound for face space dimension and this might be lowered by better constructed "eigenfaces” and by talented observers.
Index Terms:
Face and gesture recognition, computational models of vision, psychology, singular value decomposition.
Citation:
Marsha Meytlis, Lawrence Sirovich, "On the Dimensionality of Face Space," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 7, pp. 1262-1267, July 2007, doi:10.1109/TPAMI.2007.1033
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