Face age estimation method based on deep learning

Abstract

The present invention provides a face age estimation method based on deep learning. The method comprises: S1: establishing a deep learning network model; S2: employing a classification mode to perform pre-training of the deep learning network model to allow the deep learning network model to have a classification capacity; S3: performing fine regulation on the basis of the step S2, and allowing the deep learning network model to have capabilities of learning of an appearance age and estimation of the appearance age; S4: using 80% of a data set in a real age data set as a training set to perform fine regulation of the deep learning network model, and using the 20% of the data set in the real age data set as a test set to perform the test of the deep learning network model; S5: constructing an indirect regression method to estimate an age value on the basis of the software output value of the last one full connection layer in a convolutional neural network model; and S6: inputting a detected face image, and obtaining the face age of the detected face image.

Description

technical field [0001] The present invention relates to the technical field of computer identification, in particular to a method for estimating the age of a human face based on deep learning. Background technique [0002] In recent years, with the rise of deep learning, it has been widely used in many fields, especially in the field of computer vision. Deep learning is derived from the research of artificial neural networks, and including multiple hidden layers is a structure of deep learning. Deep learning is to combine low-level features to form more abstract high-level representation category attributes or features, and to use multi-layer modeling to discover the distribution characteristics of data. Due to the structural characteristics of deep learning, it is especially suitable for the field of image processing. [0003] However, because the existing age image data sets are all small data sets, overfitting occurs in the process of applying the deep transfer learning...

AI Technical Summary

Problems solved by technology

[0003] However, because the existing age image data sets are all small data sets, overfitting occurs in the process of applying the deep transfer learning network model, resulting in a large deviation between the measured age and the actual age.

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