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| Format: | Recurso digital |
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Zenodo
2020
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| Online Access: | https://doi.org/10.5281/zenodo.18693321 |
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| _version_ | 1866901635095068672 |
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| author | Meghana P S Akhila S |
| author_facet | Meghana P S Akhila S |
| contents | Biometric systems are widely used in many application for authorization of a person which is based on either behavioral characteristics or physical. Face liveness detection technique is used in various authentication scenarios (Adhar card, passport, swiping badges for entry and exit, etc.). And the face can be spoofed using various methods such as screenshot, taking a picture of a 2D face. The proposed work is to discuss the method to distinguish the person's face is live or non-live face. This method depends on the diffused patterns of live and fake faces. To extract the face features, RGB image is converted to HSV,(hue, saturation, value) it's mean and standard deviations are calculated. The pattern skewness is calculated at each pixel position to apply liveness detection algorithm as a input for the linear support vector machine for classification. By last, proposed work effectively distinguishes the live and non-live face using MATLAB tool. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18693321 |
| institution | Zenodo |
| language | |
| publishDate | 2020 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Face Liveness Detection based on Local Diffused Patterns Meghana P S Akhila S Features Skewness histogram Point spread function Support vector machine Biometric systems are widely used in many application for authorization of a person which is based on either behavioral characteristics or physical. Face liveness detection technique is used in various authentication scenarios (Adhar card, passport, swiping badges for entry and exit, etc.). And the face can be spoofed using various methods such as screenshot, taking a picture of a 2D face. The proposed work is to discuss the method to distinguish the person's face is live or non-live face. This method depends on the diffused patterns of live and fake faces. To extract the face features, RGB image is converted to HSV,(hue, saturation, value) it's mean and standard deviations are calculated. The pattern skewness is calculated at each pixel position to apply liveness detection algorithm as a input for the linear support vector machine for classification. By last, proposed work effectively distinguishes the live and non-live face using MATLAB tool. |
| title | Face Liveness Detection based on Local Diffused Patterns |
| topic | Features Skewness histogram Point spread function Support vector machine |
| url | https://doi.org/10.5281/zenodo.18693321 |