A combining approach for 2D face recognition application on IV2 database

It is often difficult to deal with the problem of 2D-face recognition under unconstrained conditions. The objective of this study is to develop an original method that overcomes such obstructions. The proposed approach combines a holistic method, the Principal Component Analysis (PCA) to a local method, the Steerable Pyramid (SP). All tests were run on IV
²
database, with challenging variability and including 3500 to 5000 comparisons by experiment from 315 different people. The followed protocol was established in the first evaluation campaign on 2D-face images using the multimodal IV
²
database. Comparison with five submitted algorithms as PCA, LDA and LDA/Gabor provides satisfying results.