What are the three classic algorithms for face recognition?

The three classic algorithms for face recognition are: Eigenface, Local Binary Patterns (LBP), and Fisherface algorithm.

Three classic algorithms for face recognition

Eigenface method ( Eigenface)

Eigenface technology is a recently developed method for face or general rigid body recognition and other methods involving face processing. The method of using eigenfaces for face recognition was first proposed by Sirovich and Kirby (1987) ("Low-dimensional procedure for the characterization of human faces") and used for face classification by Matthew Turk and Alex Pentland ("Eigenfaces for recognition" ). First, a batch of face images is converted into a set of feature vectors, called "Eigenfaces", which are the basic components of the initial training image set. The recognition process is to project a new image into the eigenface subspace, and determine and identify it through the position of its projection point in the subspace and the length of the projection line.

After transforming the image into another space, images of the same category converge together, while images of different categories converge far away. Images of different categories in the original pixel space In terms of distribution, it is difficult to divide them with simple lines or surfaces. By transforming them into another space, they can be separated well.

The spatial transformation method selected by Eigenfaces is PCA (principal component analysis). PCA is used to obtain the main components of face distribution. The specific implementation is to perform eigenvalue decomposition of the covariance matrix of all face images in the training set. The corresponding eigenvectors are obtained, and these eigenvectors are "eigenfaces". Each eigenvector or eigenface is equivalent to capturing or describing a change or characteristic between human faces. This means that each face can be represented as a linear combination of these eigenfaces.

Local Binary Patterns (LBP)

Local binary patterns (LBP) are visual algorithms used for classification in the field of computer vision. son. LBP, an operator used to describe image texture features, was proposed by T. Ojala et al. at the University of Oulu, Finland, in 1996 ("A comparative study of texture measures with classification based on featured distributions"). In 2002, T. Ojala et al. published another article on LBP ("Multiresolution gray-scale and rotation invariant texture classification with local binary patterns") on PAMI. This article very clearly explains the improved LBP features of multi-resolution, gray scale invariance, rotation invariance, and equivalent modes. The core idea of ​​LBP is to use the gray value of the central pixel as the threshold and compare it with its field to obtain the corresponding binary code to represent the local texture features.

#LBP extracts local features as the basis for discrimination. The significant advantage of the LBP method is that it is insensitive to illumination, but it still does not solve the problems of posture and expression. However, compared with the eigenface method, the recognition rate of LBP has been greatly improved.

Fisherface algorithm

Linear discriminant analysis considers category information while reducing dimensionality. It was invented by statistician Sir R. A. Fisher in 1936 ("The use of multiple measurements in taxonomic problems" 》). In order to find a way to combine features to achieve the maximum inter-class dispersion and the minimum intra-class dispersion. The idea is simple: in a low-dimensional representation, the same classes should be clustered tightly together, while different classes should be as far apart as possible. In 1997, Belhumer successfully applied the Fisher discriminant criterion to face classification and proposed the Fisherface method based on linear discriminant analysis ("Eigenfaces vs. fisherfaces: Recognition using class specific linear projection").

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