![](https://csdnimg.cn/release/download_crawler_static/16107334/bg4.jpg)
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