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There are many methods of face recognition. Here are some main methods of face recognition.

(1) face recognition method based on geometric features.

Geometric features can be the shape of eyes, nose, mouth, etc., and the geometric relationship between them (such as the distance between them). These algorithms have the advantages of fast recognition speed and small memory, but the recognition rate is low.

(2) face recognition method based on eigenface (PCA).

Eigenface method is a face recognition method based on KL transform, and KL transform is an optimal orthogonal transform for image compression. After KL transformation, a new set of orthogonal bases is obtained in high-dimensional image space, and the important orthogonal bases are retained, which can be expanded into low-dimensional linear space. If we assume that the projections of human faces in these low-dimensional linear spaces are separable, these projections can be used as feature vectors for recognition, which is the basic idea of eigenface method. These methods require more training samples and are entirely based on the statistical characteristics of image grayscale. At present, there are some improved eigenface methods.

(3) face recognition method based on neural network.

The input of neural network can be the face image with reduced resolution, the autocorrelation function of local region, the second moment of local texture and so on. This kind of method also needs more samples for training, but in many applications, the number of samples is very limited.

(4) face recognition method based on elastic image matching

The elastic map matching method defines a distance which is invariant to the usual face deformation in two-dimensional space, and uses the attribute topological graph to represent the human face. Any vertex of the topological graph contains a feature vector. It is used to record the information of the human face near the vertex position. This method combines grayscale characteristics and geometric factors, allows elastic deformation in the image during comparison, and achieves good results in overcoming the influence of facial expression changes on recognition. At the same time, multiple samples are no longer needed for a single person.

(5) face recognition method based on line segment Hausdorff distance (LHD)

Psychological research shows that human beings are no worse than grayscale images in recognizing contours (such as comics) in terms of speed and accuracy. LHD is based on the line segment graph extracted from the grayscale image of human face. It defines the distance between two line segment sets. What is different is that LHD does not establish the one-to-one correspondence between different line segment sets, so it is more adaptable to the small changes between line segment graphs. The experimental results show that LHD performs very well under different lighting conditions and different postures, but its recognition effect is not good in the case of large expression.

(6) face recognition based on support vector machine (SVM).

In recent years, support vector machine (SVM) is a new hot spot in the field of statistical pattern recognition, which tries to make the learning machine achieve a compromise in empirical risk and generalization ability, so as to improve the performance of learning machine. Support vector machine mainly solves a two-classification problem, and its basic idea is to transform a low-dimensional linear inseparable problem into a high-dimensional linear separable problem. The usual experimental results show that SVM has a good recognition rate, but it requires a large number of training samples (300 in each category), which is often unrealistic in practical applications. Moreover, the training time of support vector machine is long, and the realization of the method is complex, so there is no unified theory for the selection of this function.

There are many methods of face recognition, and one of the current research directions is the fusion of multiple methods to improve the recognition rate.

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