Face beauty prediction method and device based on adversarial transfer learning

Abstract

The invention discloses a face beauty prediction method and device based on adversarial transfer learning. The face beauty prediction method comprises the following steps: screening a face beauty prediction model with the highest correlation from a plurality of auxiliary tasks for recognizing face factors through similarity measurement, and constructing the first human face beauty prediction modelaccording to the face beauty prediction model; migrating universal characteristic parameters formed after adversarial network pre-training to a second face beauty prediction model; and inputting a to-be-detected face image to realize recognition. The training cost of pre-training is reduced, and negative migration caused by auxiliary tasks with unrelated factors is reduced; and through adversarial transfer learning, the calculation amount of retraining of the second face beauty prediction model is reduced, and the effect of obtaining a more accurate model by using fewer training images is achieved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a face beauty prediction method and device based on adversarial transfer learning. Background technique [0002] Face beauty prediction technology has been widely used in the field of photography. At the same time, with the development of deep learning technology, the application of deep learning technology to face beauty prediction technology makes the face beauty prediction results more accurate and more in line with people's cognition. However, single-task learning ignores the relationship between tasks, and multi-task learning adds unnecessary combinations to the deep learning network, which increases the redundancy of deep learning tasks and increases the burden of network training, seriously affecting Classification efficiency. Contents of the invention [0003] The purpose of the present invention is to solve at least one of the technical problems in the prior art, prov...

AI Technical Summary

Problems solved by technology

However, single-task learning ignores the correlation between tasks, and multi-task learning adds unnecessary combinations to the deep learning network, which increases the redundancy of deep learning tasks and increases the burden of network training, seriously affecting The efficiency of classification recognition

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