OCT fingerprint section image authenticity detection method based on reconstruction difference
The invention discloses an OCT fingerprint section image authenticity detection method based on reconstruction difference, and the method comprises the steps: S1, constructing a full convolutional neural network model which comprises an encoder, a generator and a feature extractor; s2, collecting im...
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| creator | ZHU CHENGFANG ZHANG YILONG LIANG RONGHUA CHEN PENG WANG HAIXIA |
| description | The invention discloses an OCT fingerprint section image authenticity detection method based on reconstruction difference, and the method comprises the steps: S1, constructing a full convolutional neural network model which comprises an encoder, a generator and a feature extractor; s2, collecting images collected by an OCT system, and after preprocessing is completed, randomly selecting 70% of positive sample images as training data; selecting the other 30% of positive sample images and negative sample images, and taking the images as test data after quantity equalization; s3, training a network model; selecting the divided training image as input data, and setting a loss function for optimizing an encoder and a generator; setting comparison loss for optimizing the feature extractor; performing multi-round training on the established network model, updating and optimizing model weight parameters through back propagation until a loss function tends to converge, and stopping training; s4, testing the network mo |
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s2, collecting images collected by an OCT system, and after preprocessing is completed, randomly selecting 70% of positive sample images as training data; selecting the other 30% of positive sample images and negative sample images, and taking the images as test data after quantity equalization; s3, training a network model; selecting the divided training image as input data, and setting a loss function for optimizing an encoder and a generator; setting comparison loss for optimizing the feature extractor; performing multi-round training on the established network model, updating and optimizing model weight parameters through back propagation until a loss function tends to converge, and stopping training; s4, testing the network mo</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNirEKwkAQBdNYiPoP6wdYhKhoKUGx0ia14bx7lyyYveNuU_j3CuYDrIZhZl487nVDnqVDiolFKcMqByEeTAcyo_YQZcv6Jged4gDtg6OnyXD09QQbJGsaf9mx90gQi2Ux8-aVsZq4KNaXc1NfN4ihRY7GQqBtfSvL7e5QHvfVqfrn-QCOkz1b</recordid><startdate>20220603</startdate><enddate>20220603</enddate><creator>ZHU CHENGFANG</creator><creator>ZHANG YILONG</creator><creator>LIANG RONGHUA</creator><creator>CHEN PENG</creator><creator>WANG HAIXIA</creator><scope>EVB</scope></search><sort><creationdate>20220603</creationdate><title>OCT fingerprint section image authenticity detection method based on reconstruction difference</title><author>ZHU CHENGFANG ; 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | OCT fingerprint section image authenticity detection method based on reconstruction difference |
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