Image processing method, device and server

Abstract

The embodiment of the present invention discloses an image processing method, device, and server, including the following steps: acquiring a face image to be processed; inputting the face image into a convolutional neural network model constructed with a loss function, the loss The function increases the inter-class distance after image classification according to the preset expected directivity screening; obtains the classification data output by the convolutional neural network model, and performs content understanding on the face image according to the classification data. By constructing a new loss function on the convolutional neural network model, the loss function has the function of increasing the class distance after image classification, and the convolutional neural network model obtained through the training of the loss function, the class difference of the output classification data As the distance increases, the distance between classes in the image recognition process increases, and the significance of the difference between images is significantly improved, resulting in a significant increase in the accuracy of image comparison, and the security of the image processing method is also 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...

AI Technical Summary

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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