Human face identification method and system
A technology of face recognition and category labeling, which is applied in the field of face recognition methods and systems, and can solve the problems of low recognition rate of face recognition methods
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Embodiment 1
[0061] see figure 1 , figure 1 It is a flow chart of a face recognition method disclosed in the embodiment of this application.
[0062] Such as figure 1 As shown, the method includes:
[0063] Step 101: Perform initial dimensionality reduction on the training sample set by PCA, obtain the initial dimensionality reduction training sample set, and save the primary projection matrix in the initial dimensionality reduction process;
[0064] Specifically, principal component analysis (PCA) is an existing dimensionality reduction method, which can provide a primary projection matrix used in the dimensionality reduction process, and perform dimensionality reduction processing on training samples through the primary projection matrix.
[0065] Step 102: using the category label information of the training samples to construct a matrix with classification information;
[0066] Specifically, each training sample corresponds to a category label, and the category label indicates the ...
Embodiment 2
[0074] In this embodiment, the above steps will be described in detail.
[0075] (1) Use PCA to perform initial dimensionality reduction on the training sample set, obtain the initial dimensionality reduction training sample set, and save the primary projection matrix during the initial dimensionality reduction process.
[0076] Specifically, define the training sample set as x i ∈R D ,y i = {1,2,...,c} is the training sample x i category label information, where D is the dimension of the training sample, l is the number of training sample data, and c is the category number of the training sample data;
[0077] Use the principal component analysis dimensionality reduction method PCA to perform initial dimensionality reduction on the training samples, and obtain the initial dimensionality reduction training sample set { x ‾ i , y i } ...
Embodiment 3
[0104] see figure 2 , figure 2 It is a schematic structural diagram of a face recognition system disclosed in the embodiment of this application.
[0105] Corresponding to the face recognition method disclosed in the above embodiment, this embodiment discloses a face recognition system, such as figure 2 Shown:
[0106] The initial dimensionality reduction unit 21 is used to perform initial dimensionality reduction on the training sample set by principal component analysis (PCA), obtain an initial dimensionality reduction training sample set, and save a projection matrix in the initial dimensionality reduction process;
[0107] The analysis information matrix construction unit 22 is used to construct a matrix with classification information by using the category label information of the training samples;
[0108] The secondary dimensionality reduction unit 23 is configured to determine an optimal secondary projection matrix, and perform secondary dimensionality reduction on...
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