Learning Discriminative Representation for ECG Biometrics Based on Multi-Scale 1D-PDV
| Autor: | Yuwen Huang, Gongping Yang, Yilong Yin, Kuikui Wang, Sun Yanwen |
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| Rok vydání: | 2019 |
| Předmět: |
Biometrics
business.industry Computer science Feature vector Cosine similarity ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Codebook 020207 software engineering Pattern recognition 02 engineering and technology ComputingMethodologies_PATTERNRECOGNITION Feature Dimension Discriminative model Feature (computer vision) Histogram 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Biometric Recognition ISBN: 9783030314552 CCBR |
| Popis: | ECG has drawn increasing attention in the biometrics and achieves great success compared with other biological characteristics. However, ECG cannot satisfy the requirements of mobile application owing to the poor quality. In this paper, we learn discriminative representation for ECG biometrics based on multi-scale 1D-PDV feature. First, we choose PDV as the base feature and attempt to convert PDV to the one-dimensional and multi-scale in the ECG biometrics. Second, our method learns a mapping to project the multi-scale 1D-PDV to a low dimensional feature vector and capture discriminative information of ECG. Then each feature vector is pooled in the codebook and represented as a histogram feature. Last, we apply principal component analysis (PCA) to reduce the histogram feature dimension and compute the matching score with cosine similarity. We evaluate our method on two public databases and the results prove our method achieves superior performance than other existing methods. |
| Databáze: | OpenAIRE |
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