Gender identification method based on face shape and generalized self-organizing mapping
A self-organizing mapping and gender recognition technology, applied in the field of face recognition, can solve problems such as the adverse effects of changes in facial posture, expression, and lighting conditions on recognition accuracy, and achieve the effect of improving recognition rate and accuracy.
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[0081] Example: Based on the MAX PLANCK 3D library, using PGA, take the first 60 eigenvectors with the largest eigenvalues to create a 3D face shape restoration model; the output layer neuron configuration of G-SOM is 30 20 regular hexagonal grid; follow 8 8 to block the NeedleMap in MAX PLANCK 3D and input it into G-SOM for training until convergence.
[0082] To verify the effect, we are using a subset of FERET, which contains 200 images of 200 people (103 males, 97 females, one image per person). Each image in the data set is preprocessed: geometric operations such as shearing, rotation, scaling, and alignment are used to obtain a final resolution of 142 124 images, and perform histogram equalization on the images. Then, use such as figure 1 The recognition process shown: For a given 2D face image, after recovering the 3D shape, use G-SOM to recognize the result. Experimental results show that the method used in this patent has achieved a recognition accuracy of 90...
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