Abstract
We describe a novel approach for 3D ear biometrics using video. A series of frames are extracted from a video clip and the region-of-interest (ROI) in each frame is independently reconstructed in 3D using Shape from Shading (SFS). The resulting 3D models are then registered using the Iterative Closest Point (ICP) algorithm. We iteratively consider each model in the series as a reference and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips. The results (95.0% rank-1 recognition rate) indicate that the proposed approach can produce recognition rates comparable to systems that use 3D range data. To the best of our knowledge, we are the first to develop a 3D ear biometric system that obtains 3D ear structure from a video sequence.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Pattern Recognition |
| State | Published - Dec 1 2008 |
| Event | 2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States Duration: Dec 8 2008 → Dec 11 2008 |
Other
| Other | 2008 19th International Conference on Pattern Recognition, ICPR 2008 |
|---|---|
| Country | United States |
| City | Tampa, FL |
| Period | 12/8/08 → 12/11/08 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
3D ear modeling and recognition from video sequences using shape from shading. / Cadavid, Steven; Abdel-Mottaleb, Mohamed.
Proceedings - International Conference on Pattern Recognition. 2008. 4761876.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - 3D ear modeling and recognition from video sequences using shape from shading
AU - Cadavid, Steven
AU - Abdel-Mottaleb, Mohamed
PY - 2008/12/1
Y1 - 2008/12/1
N2 - We describe a novel approach for 3D ear biometrics using video. A series of frames are extracted from a video clip and the region-of-interest (ROI) in each frame is independently reconstructed in 3D using Shape from Shading (SFS). The resulting 3D models are then registered using the Iterative Closest Point (ICP) algorithm. We iteratively consider each model in the series as a reference and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips. The results (95.0% rank-1 recognition rate) indicate that the proposed approach can produce recognition rates comparable to systems that use 3D range data. To the best of our knowledge, we are the first to develop a 3D ear biometric system that obtains 3D ear structure from a video sequence.
AB - We describe a novel approach for 3D ear biometrics using video. A series of frames are extracted from a video clip and the region-of-interest (ROI) in each frame is independently reconstructed in 3D using Shape from Shading (SFS). The resulting 3D models are then registered using the Iterative Closest Point (ICP) algorithm. We iteratively consider each model in the series as a reference and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips. The results (95.0% rank-1 recognition rate) indicate that the proposed approach can produce recognition rates comparable to systems that use 3D range data. To the best of our knowledge, we are the first to develop a 3D ear biometric system that obtains 3D ear structure from a video sequence.
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M3 - Conference contribution
AN - SCOPUS:77957955850
SN - 9781424421756
BT - Proceedings - International Conference on Pattern Recognition
ER -