Image processing method and apparatus, and server

Abstract

Embodiments of the invention disclose an image processing method and apparatus, and a server. The method comprises the following steps of obtaining a to-be-processed human face image; inputting the human face image to a convolutional neural network model with a loss function, wherein the loss function directionally screens and increases a between-class distance after image classification accordingto a preset expectation; and obtaining classification data output by the convolutional neural network model, and according to the classification data, performing content understanding on the human face image. The new loss function is established on the convolutional neural network model and has the effect of screening and increasing the between-class distance after the image classification; and the between-class distance of the classification data output by the convolutional neural network model obtained by training through the loss function is increased, so that the between-class distance inan image identification process is increased, the saliency of difference between images is remarkably improved, the image comparison accuracy is remarkably improved, and the security of applying theimage processing method is effectively guaranteed.

Description

technical field [0001] Embodiments of the present invention relate to the field of image processing, in particular to an image processing method, device and server. Background technique [0002] Face recognition refers to the technology of using computers to process, analyze and understand face images to identify targets and objects in various face images. Face recognition can be applied in many fields such as security and finance. The process of face recognition is generally divided into three stages: face detection, face alignment, face feature extraction and comparison, and face feature extraction is the process of face recognition. key technologies. [0003] With the development of deep learning technology, the convolutional neural network has become a powerful tool for extracting face features. For the convolutional neural network with a fixed model, the core technology is how to design the loss function so that it can effectively supervise the convolutional neural net...

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Problems solved by technology

[0004] However, the inventors of the present invention found in further research that because the loss function of Softmax Loss+Center Loss only pays attention to the distance of intra-class features and ignores the distance of inter-class features, the distance between classes is not prominent enough when comparing graphs , resulting in low image recognition accuracy during image comparison and a high probability of misjudgment, making it impossible to apply image recognition technology to areas with high security requirements

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