Facial image age estimation method based on three-level residual error network
A face image and residual technology, applied in the field of data processing, can solve problems such as gradient disappearance, hinder classification model learning process, limit DCNN network learning ability, etc., to solve overfitting and gradient disappearance, improve learning ability, improve The effect of accuracy
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[0022] The present invention will be further described below in conjunction with accompanying drawing.
[0023] In order to solve the current problems of face age estimation, the present invention establishes a three-level residual network, and uses the three-level residual network to pre-train the ImageNet data set, and then uses the three-level residual network to perform pre-training on the face age estimation data set under unrestricted conditions. The pre-trained model is fine-tuned, and the random depth algorithm is used to suppress the over-fitting problem during the fine-tuning process.
[0024] The present invention comprises following 3 steps:
[0025] 1. First, a three-level residual network is established on the basis of the basic residual network framework to improve the learning ability of the DCNN network model.
[0026] 2. Limited to the size of the face age estimation data set, the ImageNet data set is pre-trained using a three-level residual network to obtai...
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