Training method of convolutional neural network (CNN) and face identification method and device

Abstract

The present invention discloses a training method of a convolutional neural network and a face identification method and device. The training method is characterized by determining a first error by adopting a first joint training supervision function composed of a cross-entropy loss function and a comparison loss function after the normalization and having two threshold values and according to thecharacteristic vectors of the face images in the samples of a source domain training sample set, and adjusting the network parameters of the convolutional neural network via the first error, whereinthe first threshold value is used to compare with the Euclidean distance of the characteristic vectors of the two face images in a positive sample pair, and the second threshold value is used to compare with the Euclidean distance of the characteristic vectors of the two face images in a negative sample pair, so that the supervised training of the negative sample pair can be controlled, and the supervised training of the positive sample pair also can be controlled, and the training efficiency and the accuracy of the CNN are improved, and accordingly, the generalization ability of the face identification method can be improved when the trained CNN is applied to the face identification method.

Description

technical field [0001] The present invention relates to the technical field of deep learning, in particular to a training method of a convolutional neural network, a face recognition method and a device. Background technique [0002] At present, in order to better recognize face images, more and more recognition processes need to use Convolutional Neural Network (CNN). In order to ensure accurate recognition results, CNN needs to be trained repeatedly. At present, the contrastive loss function (Contrastive Loss) with a single threshold is generally used to train CNN. However, since this contrastive loss function only controls the convergence of the negative sample pairs in the sample set by a single threshold to train the CNN, it does not control the convergence of the positive sample pairs in the sample set. Since there are a large number of positive sample pairs that are easy to converge in the sample set, and these positive sample pairs are not supervised for training, ...

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Problems solved by technology

However, since this contrastive loss function only controls the convergence of the negative sample pairs in the sample set by a single threshold to train the CNN, it does not control the convergence of the positive sample pairs in the sample set

Since there are a large number of positive sample pairs that are easy to converge in the sample set, and these positive sample pairs are not supervised for training, it not only increases the burden of CNN, but also hinders CNN from supervised learning of other positive sample pairs that are not easy to converge, making CNN The training efficiency and accuracy are reduced, which leads to less than ideal training results of CNN

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