Multi-source domain adaptive face recognition method
A face recognition and domain adaptation technology, applied in the field of face recognition involving domain adaptation, can solve the problems of training classifiers, no sample data, neglect and so on
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[0046] Experimental data set: MultiPIE has a total of 337 categories with approximately 750,000 pictures containing different angles and different lighting. In this experiment, select (-45°, -30°, -15°, 0°, 15°, 30°, 45°) these 7 angles. The 337 categories are divided into 200 and 137 categories, 200 categories are used as training categories, and 137 categories are used as test categories. In the training sample, 7 pictures are randomly selected for each category, in the test sample, 1 picture is taken for each category in the picture library, and 4 pictures are randomly selected for the test sample. As shown in Table 2, take the source domain as -30° and the target domain as 45° as an example.
[0047] Table 1 Example dataset setup
[0048] .
[0049] Align the facial images according to the manually marked eye positions, and normalize the pixels of all pictures to 40×32 pixels. The features of each picture are represented by column vectors, and PCA is used for dimensio...
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