Fingerprint detection classification method based on space transformation convolutional neural network

Abstract

The invention discloses a fingerprint detection classification method based on the space transformation convolutional neural network. The fingerprint detection classification method comprises a fingerprint image extraction region of interest preprocessing, image high-frequency region extraction, image space transformation processing and convolution neural network classification training and testing. The fingerprint image extraction region of interest preprocessing removes a blank region through extracting a fingerprint part in an image; the high-frequency region extraction means the high-frequency characteristic of the image is extracted through a gaussian high-pass filter; as for the image space transformation processing, the space transformation neural network is used for carrying out translation, cutting and rotating operation on the input image, so that expansion of image data is achieved; the convolution neural network adopts multi-layer convolution pooling, convolution kernels with different sizes are used for extracting image features, and a good classification detection effect is obtained on the test set. The invention provides a fingerprint detection method which is low incost, high in detection precision and short in time consumption.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a fingerprint detection and classification method based on a spatial transformation convolutional neural network. Background technique [0002] At present, the fingerprint identification system plays a huge role in many fields such as finance, access control and personnel management. Fingerprint detection technology is widely used in mobile phone unlocking, fingerprint attendance and other fields. However, human fingerprints are very easy to be forged. Only gelatin, paraffin and other materials can be used to produce artificial fingerprints that can deceive fingerprint recognition systems, which brings great potential safety hazards to personal and property safety. [0003] Existing fingerprint detection techniques can be divided into two categories. The first type is a hardware-based solution, which collects information such as finger skin temperature, c...

AI Technical Summary

Problems solved by technology

However, the convolutional neural network is very dependent on the provided image data and requires a large amount of data for training. The current training process of the convolutional neural network is complicated and the amount of data is very large, which also results in long training time and high time cost.

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