Three face databases were tested: Yale database, Olivetti Research Laboratory (ORL) database, and the PIE (pose, illumination, and expression) database from CMU [24].
In the second segment, we investigated the inter database homogeneity by forming a hybrid face database drawn from the BU-3DFE and UPM-3DFE.
A generic face database is obtained from the FRGC v2.0 in such a way that each subject has at least 10 sample face images.
Centered passport-type details of 576×576 pixels (AR face database) were cut from each image for analysis.
The last segment of The Face Field Project is The Face Synth, a system for designing a whole new faces from scratch, based on information gathered from the face database.
The experiment is implemented in MATLAB R2013a, and we have tested 500 face images from AR face database.
Figure 4 Face detection by using local SMQT for three samples from Oulu face database.
Figure 2 Sample images with different illumination from Oulu face database after and before illumination enhancement.
The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively.
Figure 4 shows the images of the three faces from Oulu face database enhanced by using DWT + SVE illumination enhancement and the segmented faces by using local SMQT.
