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Subspace Learning Codes (Matlab) and Datasets for Face Recognition
We provide here some matlab codes of subspace learning algorithms, as
well as some datasets in matlab format. All these codes and data sets
are used in our experiments. The processed data in matlab format can
only be used for non-commercial purpose.
If you have some problems or find some bugs in the codes, please email: dengcai2 AT cs DOT uiuc DOT edu
Learning algorithms
All the codes have been rewritten. You can download the old version at here .
I am polishing the codes. If some files are missing, I am polishing them. Please wait some days...
- Some general functions
- EuDist2: Calculate the Euclidean distance matrix of two data matrix.
- ConstructKernel: Construct the kernel matrix.
- PCA: Principal Component Analysis
- KPCA: Kernel Principal Component Analysis
- LGE:
(Regularized) Linear Graph Embedding (Provides a general framework for
graph based subspace learning. This function will be called by LPP,
NPE, IsoProjection, LSDA, MFA ...)
- OLGE:
(Regularized) Orthogonal Linear Graph Embedding (Provides a general
framework for graph based subspace learning (orthogonal basis vectors).
This function will be called by OLPP. It is also very easy to develop
ONPE, OIsoProjection, OLSDA, OMFA ...)
- TensorLGE:
Tensor Linear Graph Embedding (Provides a general framework for graph
based tensor subspace learning. This function will be called by
TensorLPP. It is also very easy to develop TensorNPE,
TensorIsoProjection, TensorLSDA, TensorMFA ...)
- KGE:
(Regularized) Kernel Graph Embedding (Provides a general framework for
graph based kernel subspace learning. This function will be called by
KernelLPP. It is also very easy to develop KernelNPE,
KernelIsoProjection, KernelLSDA, KernelMFA ...)
- LDA:
(Regularized) Linear Discriminant Analysis (Generally, LDA can also use
LGE as a subroutine. However, we can use the special graph structure of
LDA to obtain some computational benefits.)
- KDA:
(Regularized) Kernel Discriminant Analysis (Generally, KDA can also use
KGE as a subroutine. However, we can use the special graph structure of
KDA to obtain some computational benefits.)
- constructW: Function used to construct the affinity matrix.
- LPP: Locality Preserving Projection (You need to download LGE.m as well as constructW.m).
- OLPP: Orthogonal Locality Preserving Projections (You need to download OLGE.m as well as constructW.m)
- TensorLPP: Tensor Locality Preserving Projections (You need to download TensorLGE.m as well as constructW.m)
- KernelLPP: Kernel Locality Preserving Projections (You need to download KGE.m as well asconstructW.m)
- NPE: Neighborhood Preserving Embedding (You need to download LGE.m)
- IsoProjection: Isometric Projection (You need to download LGE.m)
- LSDA: Locality Sensitive Discriminant Analysis (You need to download LGE.m)
- MFA: Marginal Fisher Analysis (You need to download LGE.m)
Some popular face database in matlab format
All the faces are manually aligned, cropped and resize to 32x32 pixels.
Each matlab data file contain variables ‘fea‘ and ‘gnd‘. ‘fea‘ is the data, each row is a sample (face); ‘gnd‘ is the
label.
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.
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).
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.
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.
Here are the databases with size of 64x64 pixels. (Matlab 7 or higher version is required to open these files)
Yale
ORL
PIE Pose C05 | PIE Pose C07 | PIE Pose C09 | PIE Pose C27 | PIE Pose C29
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