We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space—if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher’s Linear Discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed “Fisherface” method has error rates that are lower than those of the Eigenface technique for tests on the Harvard and Yale Face Databases.
When benchmarking an algorithm it is recommendable to use a standard test data set for researchers to be able to directly compare the results. While there are many databases in use currently, the choice of an appropriate database to be used should be made based on the task given (aging, expressions, lighting etc). Another way is to choose the data set specific to the property to be tested (e.g. how algorithm behaves when given images with lighting changes or images with different facial expressions).
- 3D_RMA database
- AMP-CMU
- AR (Aleix)
- AT&T
- BANCA
- BFM
- BioID
- BJUT-3D
- Bosphorus 3D Database
- CALTECH
- CAS-PEAL
- CMU Cohn-Kanade AU
- CMU PIE
- CMU VASC
- CVL
- EQUINOX HID
- ESSEX
- FERET
- FG-NET Aging
- FG-NET Talking face
- FRAV2D
- FRAV3D
- FRGC
- GavabDB
- Georgia Tech
- ICPR
- Image Database of Facial Actions and Expressions
- Indian Face Database
- JAFFE
- Labeled Faces in the Wild
- LFW
- MAX PLANCK
- MIT-CBCL
- MIT-CSAIL
- NIST MID
- NLPR
- OULU
- Plastic Surgery Face Database
- PICS
- SCface
- UCFI (UCD)
- USENIX (ftp)
- VALID
- VidTIMIT
- VIOLA training set
- WEIZMANN
- XM2VTS
- YALE A
- YALE B
Index Terms: face, database, databases, download, list.
 | Figure 1. Face database | ||||||||||
A complete list of public face databases available on the web. Index terms: appearance-based vision, face recognition, illumination invariance, Fisher’s linear discriminant, face recognition, face matching, face identification, PCA, principal components analysis, fisherfaces.
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