| Autor: |
Nikam, Shankar Bhausaheb, Agarwal, Suneeta |
| Zdroj: |
International Journal of Intelligent Systems Technologies and Applications; January 2009, Vol. 7 Issue: 3 p296-315, 20p |
| Abstrakt: |
Perspiration-based liveness detection method is slow, as it requires two consecutive fingerprints to notice perspiration. Some other methods in the literature need extra hardware to detect liveness. To alleviate the problems, a single-image method using fusion of Gabor features and grey level cooccurrence probability (GLCP) features is proposed. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Dimensionality of the features is reduced by principal component analysis (PCA). We test feature sets on three classifiers: AdaBoost.M1, support vector machine and alternating decision tree; then we fuse all the classifiers using the 'Max Rule' to form an ensemble classifier. Fused feature set is found to produce higher accuracy (∼98.35% classification rate) relative to the individual feature sets (classification accuracy ranges from ∼93.88% to ∼96.71%). Thus, the performance of new liveness detection approach is very promising, as it needs only one fingerprint and no extra hardware to detect vitality. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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