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Technical Program
Paper Detail
| Paper: | MP-L4.1 |
| Session: | Face/Facial Expression Detection and Recognition |
| Time: | Monday, October 9, 14:20 - 14:40 |
| Presentation: |
Lecture
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| Title: |
MULTI-MODAL FACE RECOGNITION BY MEANS OF AUGMENTED NORMAL MAP AND PCA |
| Authors: |
Andrea Abate; Università degli studi di Salerno | | | | | Michele Nappi; Università degli studi di Salerno | | | | | Stefano Ricciardi; Università degli studi di Salerno | | | | | Gabriele Sabatino; Università degli studi di Salerno | | |
| Abstract: |
Face represents a rich biometric identifier whose potential in term of discriminating power has not been fully exploited yet. This paper addresses face recognition through a multi-modal approach operating on face’s 3D (geometry) and 2D (skin texture) features by means of two different metrics: Augmented Normal Map and Principal Component Analysis. Augmented Normal Map includes shape (surface normals represented as 24 bit colour pixels) and texture info (additional 8 bit for skin colour) into one 32 bit image. The proposed two-staged method firstly performs a fast one-to-many comparison of facial geometry exploiting normal map metric. Then, to further improve recognition precision and reliability, best rank faces are compared to probe by PCA resulting in a final score. Other advantages are robustness to facial expressions and the ability to selectively filter face’s non-skin regions (beard, moustaches). We include preliminary experimental results on a dataset of 101 textured 3D faces. |
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