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Four face databases in matlab format
Algorithms
(Matlab 7 or higher version is required to open these files)
- Yale database
Contains 165 grayscale images in GIF format of 15 individuals. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink.
32x32 Data File: contains variables 'fea' and 'gnd'. Each row of 'fea' is a face; 'gnd' is the label. Sample images
64x64 Data File.
A random subset with p(=2,3,4,5,6,7,8) images per individual was taken with labels to form the training set, and the rest of the database was considered to be the testing set. For each given p, there are 50 randomly splits:
2 Train | 3 Train | 4 Train | 5 Train | 6 Train | 7 Train | 8 Train |
Each split file contains variables 'trainIdx' and 'testIdx'. The following matlab codes can be used to generate the training and test set:
%===========================================
fea_Train = fea(trainIdx,:);
fea_Test = fea(testIdx,:);
gnd_Train = gnd(trainIdx);
gnd_Test = gnd(testIdx);
%===========================================
Experimental results (new!) | old experimental results
- ORL database
Ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).
32x32 Data File: contains variables 'fea' and 'gnd'. Each row of 'fea' is a face; 'gnd' is the label. Sample images
64x64 Data File
A random subset with p(=2,3,4,5,6,7,8) images per individual was taken with labels to form the training set, and the rest of the database was considered to be the testing set. For each given p, there are 50 randomly splits:
2 Train | 3 Train | 4 Train | 5 Train | 6 Train | 7 Train | 8 Train |
Experimental results (new!) | old experimental results
- PIE database, CMU
A database of 41,368 images of 68 people, each person under 13 different poses, 43 different illumination conditions, and with 4 different expressions.
This dataset only contains five near frontal poses (C05, C07, C09, C27, C29) and all the images under different illuminations and expressions. So, there are 170 images for each individual.
32x32 Data File: contains variables 'fea' and 'gnd'. Each row of 'fea' is a face; 'gnd' is the label. Sample images
64x64 Data File: Pose C05 | Pose C07 | Pose C09 | Pose C27 | Pose C29
Pose27(lights change): Used in the paper "Laplacian Score for Feature Selection", in NIPS'05.
A random subset with p(=5,10,...,130) images per individual was taken with labels to form the training set, and the rest of the database was considered to be the testing set. For each given p, there are 50 randomly splits:
5 Train | 10 Train | 20 Train | 30 Train | 40 Train | 50 Train | 60 Train | 70 Train | 80 Train | 90 Train | 100 Train | 110 Train | 120 Train | 130 Train |
Experimental results (new!) | old experimental results
- Extended Yale Face Database B
Please refer to this link for detailed information.
For this database, we simply use the cropped images (which can be downloaded here) and resize them to 32x32 pixels. This dataset now has 38 individuals and around 64 near frontal images under different illuminations per individual.
32x32 Data File: contains variables 'fea' and 'gnd'. Each row of 'fea' is a face; 'gnd' is the label. Sample images
A random subset with p(=5,10,...,50) images per individual was taken with labels to form the training set, and the rest of the database was considered to be the testing set. For each given p, there are 50 randomly splits:
5 Train | 10 Train | 20 Train | 30 Train | 40 Train | 50 Train |
Experimental results (new!) | old experimental results
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