Face model construction method, face recognition method, device and equipment
A construction method and face model technology, applied in the field of face recognition, can solve the problems of 3D model difficulty, ignoring texture reconstruction, and inability to apply it to face recognition, so as to eliminate the difference in illumination and improve the accuracy rate
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Embodiment 1
[0067] As shown in Figure 1, the present invention provides a method for constructing a face model, and the subject of execution of the method may be a terminal device or a server. In order to improve the construction efficiency of the face recognition database and enrich the image information of the database, the present invention adopts the following steps:
[0068] Use the X-face photo of the same person to reconstruct the three-dimensional face shape, X is a positive integer, and X≥3;
[0069] This method can use photos of multiple face faces to reconstruct the face model, which can be face photos from multiple angles. This embodiment uses the three-face photo of each person in the registration database to reconstruct the three-dimensional face shape of each person. The registration database includes the three-face photo of each person. The photos are frontal photos, left face photos and right face photos. In this embodiment, three photos of N individuals are used as N se...
Embodiment 2
[0125] The difference between this embodiment and Embodiment 1 is that step S24 can be replaced by step A24, and step A24 is specifically:
[0126] After calculating the adjustment amount of the two-dimensional feature point and the adjustment amount of the three-dimensional face shape, the adjustment amount of the two-dimensional feature point is used as the input of the multi-layer perceptron network, and the adjustment amount of the three-dimensional face shape is used as the supervisory signal output by the network, Train the network until the network converges, output the update amount of the three-dimensional face shape, and use the three-dimensional face shape and the update amount of the three-dimensional face shape to calculate and update the three-dimensional face shape:
[0127] The update amount ΔS of the three-dimensional face shape output by the multi-layer perceptron network k+1 , calculate the 3D face shape S after the k+1th completion k+1 :
[0128] S k+1 =...
Embodiment 3
[0141] Use the three-dimensional photo and the reconstructed 3D face shape to reconstruct the texture, obtain the texture reconstructed 3D face shape, and complete the construction of the 3D face model;
[0142] In order to further reconstruct the complete texture information of the 3D face, this step assigns a corresponding texture value to each point in the 3D face shape obtained in the previous stage.
[0143] Such as Figure 4 Shown is the flow chart of 3D face texture reconstruction. Texture reconstruction is divided into two stages: texture mapping and texture fusion. The texture mapping stage roughly obtains the texture value of each point, and the texture fusion stage further optimizes the texture selected in the previous stage to eliminate splicing traces or texture inconsistencies caused by factors such as lighting.
[0144] B1, texture mapping
[0145] B11. Texture mapping based on three-dimensional vertices: based on the three-dimensional face photo and the recon...
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