Pregled bibliografske jedinice broj: 203160
Statistics in Face Recognition: Analysing Probability Distributions of PCA, ICA and LDA Performance Results
Statistics in Face Recognition: Analysing Probability Distributions of PCA, ICA and LDA Performance Results // Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis / Lončarić, Sven ; Babić, Hrvoje ; Bellanger, Maurice (ur.).
Zagreb: Fakultet elektrotehnike i računarstva Sveučilišta u Zagrebu, 2005. str. 289-294 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Statistics in Face Recognition: Analysing Probability Distributions of PCA, ICA and LDA Performance Results
Autori
Delač, Krešimir ; Grgić, Mislav ; Grgić, Sonja
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis
/ Lončarić, Sven ; Babić, Hrvoje ; Bellanger, Maurice - Zagreb : Fakultet elektrotehnike i računarstva Sveučilišta u Zagrebu, 2005, 289-294
Skup
4th International Symposium on Image and Signal Processing and Analysis, ISPA 2005
Mjesto i datum
Zagreb, Hrvatska, 15.09.2005. - 17.09.2005
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Face Recognition; Statistics; Probability Distributions; PCA; ICA; LDA; FERET
Sažetak
In this paper we address the issue of evaluating face recognition algorithms using descriptive statistical tools. By using permutation methodology in a Monte Carlo sampling procedure, we investigate recognition rate results probability distributions of some well-known algorithms (namely, PCA, ICA and LDA). With a lot of contradictory literature on comparisons of those algorithms, we believe that this kind of independent study is important and will serve to better understanding how each algorithm works. We show how simplistic approach to comparing these algorithms can be misleading and propose a full statistical methodology to be used in future reports. By reporting detailed descriptive statistical results, this paper is the only available detailed report on PCA, ICA and LDA comparative performance currently available in literature. Our experiments show that the exact choice of images to be in a gallery or in a probe set has great effect on recognition results and this fact will further emphasize the importance of reporting detailed results. We hope that this study will help to advance the state of experiment design in computer vision.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
