2D-3D融合的单样本人耳识别：MKD-SRC方法与OSPP性能提升

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本文主要探讨的是"每人一个样品的耳朵识别"这一前沿的生物识别技术在身份认证中的应用。随着生物识别因其高效和便捷性在信息安全领域日益受到重视，尤其是耳朵识别作为一种有潜力的生物特征识别方法，它相较于传统的多样本（MSPP）设置，面对每个个体仅有一个样本（OSPP）的情况，面临着识别性能的挑战。作者针对OSPP问题，提出了创新的混合多关键点描述符和稀疏表示的分类（MKD-SRC）耳朵识别方法。该方法巧妙地结合了2D和3D信息，因为现代3D传感器通常同时捕捉三维数据并存储相关的二维数据。研究过程首先通过耳朵区域的轮廓检测来提取关键信息，然后对2D纹理图像和3D范围图像的关键点进行描述。这种多维度的数据处理方式有助于提高识别的准确性和鲁棒性。通过实验验证，该方法在基准数据集上展现出显著的优势。在包含415名受试者的画廊中，一级识别率达到了96.4%，这表明在OSPP条件下，MKD-SRC方法能有效解决身份验证问题。此外，与传统方法相比，该方法在保持高识别精度的同时，还表现出满意的计算效率，这对于实际应用来说是非常重要的。这篇研究论文不仅填补了在OSPP环境下耳朵识别的空白，还展示了将2D和3D信息融合在耳朵识别中的潜力，为生物识别技术的发展提供了新的思路。对于那些依赖生物特征安全的应用，如门禁系统、移动设备解锁等，这项技术的进步无疑将提升用户体验和系统的安全性。

When the OSPP problem began to draw attention from researchers, it was 3D information

that was the focus of many solutions. 3D data have an advantage over the 2D data in addressing

lighting variation and pose changes [28]. From a different perspective, 3D data can provide

more information than 2D data. Chen and Bhanu [29] proposed a complete ear recognition

system that represented 3D ears using local surface patch (LSP). A modified iterative closest

point (ICP) algorithm [30] was adopted to match the probe sample with all samples in the gal-

lery. In their later study [31], a new dimension reduction algorithm was adopted, and the simi-

larity between a model-test pair was computed in a low-dimensio nal space. They reported a

recognition rate of 96.7% in the best case. Yan and Bowyer [32] later proposed an automatic

ear recogn ition method using both 2D and 3D information. After the ear region was segmented

from a profile image, an ICP-based approach was also used for 3D shape matching. This meth-

od achieved a rank-one recognition rate of 97.8%. Islam et al.[33] proposed a fast 3D local fea-

ture matching and fine matching method via ICP. It is superior to Yan and Bowyer’s method

in terms of computational speed. These ICP-based methods required only one sample (or

model) per person in the gallery, but the computation involved in the ICP algorithm is consid-

erably intensive, and the ICP algorithm easily falls into local minima. In addition, the 3D

point cloud data require considerable storage. For instance, a segmented ear point cloud data

(as in form of. txt) is 536 kB, whereas its corresponding range image (as in form of. png) is only

10.1 kB. Thus, under the premise of satisfying performance, the range image is a better choice

for 3D data representation.

Liao et al.[34] proposed a brand new method to address the OSPP problem in face recogni-

tion. They improved the SRC method by detecting keypoints in OSPP to form a multi-keypoint

dictionary. In this manner, each subject in the gallery is represented by a group of descriptors

(the number of each subject can vary), which made it possible to adjust the SRC method to the

OSPP problem. Additionally, this method does not require an alignment, and partial data and

occlusion are not an issue because the descriptor size is determined by the actual content of the

image. Experimental results have demonstrated the feasibility of the MKD-SRC, but its perfor-

mance is not satisfying. The rank-one recognition rate on FRGCv2.0+ database is only approxi-

mately 70% [34]. Later Zhang et al.[35] extended this method to 3D face recogn ition. They

achieved high rank-one recognitino rate on differe nt databases. However, multiple samples

were still needed to form the gallery set.

Many studies on ear recognition have been conducted to date. However, as the results of the

previous studies indicate, the OSPP problem remains unresolved. Most existing techniques re-

quire multiple training samples for recognition. SIFT-based matching algorithms have failed to

achieve satisfying performance because unlike object recognition, ear recognition is an intra-

class issue. ICP-based methods perform better but at the expense of high storage overhead and

a high computational load involved in the recognition process. Thus, in this paper, we attempt

to shed new light on the OSPP problem in ear recognition by fusing the 2D texture information

and the 3D shape information into an SRC-based recognition framework.

2D and 3D Hybrid MKD-SRC

Inspired by the MKD-SRC face recognition approach [34], we propose a 2D and 3D hybrid

method for ear recognition in this paper. The proposed method distinguishes itself from others

as follows:

1. It is a robust alignment-free SRC based ear recognition approach that requires only OSPP in

the gallery.

A Hybrid Approach for 2D&3D Ear Recognition

PLOS ONE | DOI:10.1371/journal.pone.0129505 May 29, 2015 4/16

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2D-3D融合的单样本人耳识别：MKD-SRC方法与OSPP性能提升

单张图像对每人多张注册图像的人脸识别.pdf

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