A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN
| Autor: | Gan Junying, Cao He, Lu Cao, Vincenzo Piuri, Fabio Scotti, Wenbo Deng, Wang Jinxin, Junying Zeng, Yikui Zhai, Yihang Zhi, Hui Ma |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry 020208 electrical & electronic engineering Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Overfitting Convolutional neural network ComputingMethodologies_PATTERNRECOGNITION Gabor filter Knuckle medicine.anatomical_structure Discriminative model 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business Histogram equalization Coding (social sciences) |
| Zdroj: | Biometric Recognition ISBN: 9783319979083 CCBR |
| DOI: | 10.1007/978-3-319-97909-0_2 |
| Popis: | Traditional feature extraction methods, such as Gabor filter and competitive coding, have been widely used in finger-knuckle-print (FKP) recognition. However, these methods focus on manually designed features which may not achieve satisfying results on FKP images. In order to solve this problem, a novel batch-normalized Convolutional Neural Network (CNN) architecture with data augmentation for FKP recognition is proposed. Firstly, a novel batch-normalized CNN is designed specifically for FKP recognition. Then, random histogram equalization is adopted as data augmentation here for training the CNN in FKP recognition. Meanwhile, batch-normalization is adopted to avoid overfitting during network training. Extensive experiments performed on the PolyU FKP database show that compared with traditional feature extraction method, the proposed method can not only extract more discriminative features, but also improve the accuracy of FKP recognition. |
| Databáze: | OpenAIRE |
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