Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

Title: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
Author: Peter N. Belhumeur, Joao P. Hespanha, and David J. kriegman
Year of Publication: 1997
Publisher: IEEE

In this paper, the authors proposed the method, Fisherfaces, for face recognition. Fisherfaces is insensitive to large variations in illumination and facial expressions. Compared with eigenfaces which uses PCA for dimensionality reduction and maximize the total scatter across all classes, fisherfaces maximize the ratio of between-class scatter to that of the within-in class scatter. They compare four methods for recognition under variation in lighting and facial expression: correlation, a variant of the linear subspace method, the Eigenface method, and the Fisherface method.
(1) Correlation is the simplest method but need variant lighting training data and require large time complexity and storage.
(2) Eigenfaces apply PCA and reduces time complexity a lot. However, when it maximizes between-class scatter, it also maximizes with-in class scatter which is unwanted information for face recognition. It have been suggested that by discarding the three most significant principal components, the effects of variant illumination may be reduced, but it may also result in unexpected consequence.
(3) The linear subspace algorithm take the normal vector to the surface and the albedo of the surface into consideration. That is, the algorithm can easily recognize Lambertian surfaces and be insensitive to a wide range of lighting conditions. Nevertheless, it has to learn where the good regions for recognition are, and its computation and storage are higher than the Eigenfaces method.
(4) Fisherfaces use the Fisher’s Linear Discriminant method which is class specific. The approach maximize the ratio of the between-class scatter and the within-class scatter, that is, achieve greater between-class scatter and decrease within-class scatter.

In conclusion, it shows that fisherfaces perform better than other three methods in the several experiments, variant lighting, facial expression, and glasses recognition. It is based on more reasonable dimensionality reduction, and it requires lower computation by modifying the original equation with PCA. Also, it doesn’t need storage as much as the Linear Subspace method. What it can be improved is how to deal with extreme lighting condition and , maybe, side face recognition.