A fast algorithm for ICP-based 3D shape biometrics

Yan, Ping; Bowyer, Kevin W.

doi:10.1016/j.cviu.2006.11.001

Cited by 61 publications

(28 citation statements)

References 29 publications

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“…In contrast to [27] all points of the segmented ear model are used. The reported performance measures of 97.3% in [77] and 97.8% in [18] is similar but not directly comparable, because different datasets were used for evaluation.…”

Section: D Ear Recognitionmentioning

confidence: 51%

“…Although ICP is originally designed to be an approach for image registration, the Figure 8: Examples for surface features in 3D ear images. The upper image shows an example for ICP-based comparison as proposed in [27], whereas the lower figure illustrates feature extraction from voxels as described in [77].…”

Section: D Ear Recognitionmentioning

confidence: 99%

“…Yan and Browyer decompose the ear model into voxels and extract surface features from each of these voxels. For speeding up the alignment process, each voxel is assigned an index in such a way that ICP only needs to align voxel pairs with the same index [77] (see Figure 8). In [18] Yan and Browyer propose the usage of point clouds for 3D ear recognition.…”

Section: D Ear Recognitionmentioning

confidence: 99%

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Ear biometrics: a survey of detection, feature extraction and recognition methods

2012

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The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features.The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people.Because of these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, we categorize and summarize approaches to ear detection and recognition in 2D and 3D images. Then, we provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which we consider to be the most important application of ear recognition characteristic in the near future.

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Section: D Ear Recognitionmentioning

confidence: 51%

Section: D Ear Recognitionmentioning

confidence: 99%

Section: D Ear Recognitionmentioning

confidence: 99%

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Ear biometrics: a survey of detection, feature extraction and recognition methods

2012

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“…Yan and Bowyer [26] proposed a regular 3D grid of voxels that allow constant-time lookup for a closest point, by storing a single closest point per voxel. However, such fixed-size voxel grids use excessive amounts of memory and require a trade-off between memory consumption and lookup speed.…”

Section: Related Workmentioning

confidence: 99%

Almost constant-time 3D nearest-neighbor lookup using implicit octrees

Drost

Ilić

2017

Machine Vision and Applications

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A recurring problem in 3D applications is nearestneighbor lookups in 3D point clouds. In this work, a novel method for exact and approximate 3D nearest-neighbor lookups is proposed that allows lookup times that are, contrary to previous approaches, nearly independent of the distribution of data and query points, allowing to use the method in real-time scenarios. The lookup times of the proposed method outperform prior art sometimes by several orders of magnitude. This speedup is bought at the price of increased costs for creating the indexing structure, which, however, can typically be done in an offline phase. Additionally, an approximate variant of the method is proposed that significantly reduces the time required for data structure creation and further improves lookup times, outperforming all other methods and yielding almost constant lookup times. The method is based on a recursive spatial subdivision using an octree that uses the underlying Voronoi tessellation as splitting criteria, thus avoiding potentially expensive backtracking. The resulting octree is represented implicitly using a hash table, which allows finding the leaf node a query point belongs to with a runtime that is logarithmic in the tree depth. The method is also trivially extendable to 2D nearest neighbor lookups.

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“…Compared with 2D ear images, the 3D ear data are relatively insensitive to illuminations and posture variation. Therefore, ear recognition methods utilizing 3D shape information have become a recent trend in research field [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

3D Ear Normalization and Recognition Based on Local Surface Variation

Zhang

et al. 2017

Applied Sciences

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Most existing ICP (Iterative Closet Point)-based 3D ear recognition approaches resort to the coarse-to-fine ICP algorithms to match 3D ear models. With such an approach, the gallery-probe pairs are coarsely aligned based on a few local feature points and then finely matched using the original ear point cloud. However, such an approach ignores the fact that not all the points in the coarsely segmented ear data make positive contributions to recognition. As such, the coarsely segmented ear data which contains a lot of redundant and noisy data could lead to a mismatch in the recognition scenario. Additionally, the fine ICP matching can easily trap in local minima without the constraint of local features. In this paper, an efficient and fully automatic 3D ear recognition system is proposed to address these issues. The system describes the 3D ear surface with a local feature-the Local Surface Variation (LSV), which is responsive to the concave and convex areas of the surface. Instead of being used to extract discrete key points, the LSV descriptor is utilized to eliminate redundancy flat non-ear data and get normalized and refined ear data. At the stage of recognition, only one-step modified iterative closest points using local surface variation (ICP-LSV) algorithm is proposed, which provides additional local feature information to the procedure of ear recognition to enhance both the matching accuracy and computational efficiency. On an Inter ® Xeon ® W3550, 3.07 GHz work station (DELL T3500, Beijing, China), the authors were able to extract features from a probe ear in 2.32 s match the ear with a gallery ear in 0.10 s using the method outlined in this paper. The proposed algorithm achieves rank-one recognition rate of 100% on the Chinese Academy of Sciences' Institute of Automation 3D Face database (CASIA-3D FaceV1, CASIA, Beijing, China, 2004) and 98.55% with 2.3% equal error rate (EER) on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2, University of Notre Dame, South Bend, IN, USA, between 2003 and2005).

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A fast algorithm for ICP-based 3D shape biometrics

Cited by 61 publications

References 29 publications

Ear biometrics: a survey of detection, feature extraction and recognition methods

Ear biometrics: a survey of detection, feature extraction and recognition methods

Almost constant-time 3D nearest-neighbor lookup using implicit octrees

3D Ear Normalization and Recognition Based on Local Surface Variation

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