Abstract
Human palmprint-based biometric solutions have been studied extensively in both controlled and uncontrolled environments. However, the majority of existing methods do not reliably handle variations of translation, rotation, and blurriness of one's palm within the range of acceptable tolerance, which largely degrades the performance. Therefore, this paper presents a unique local descriptor called Local Micro-structure Tetra Pattern (LMTrP) and its application to palmprint recognition. The proposed descriptor takes advantage of local descriptors’ direction as well as thickness. In this paper, the palmprint image is first filtered with the line-shaped filter to effectively eliminate unnecessary features. Then, local region histograms of LMTrP are extracted and concatenated into one feature vector to represent the given image. Finally, the kernel linear discriminant analysis is applied on the feature vector for dimension reduction. The experimental results indicate that the proposed methods significantly outperform the state-of-the-art methods without the need to align the palmprint images.
| Original language | English |
|---|---|
| Pages (from-to) | 29-46 |
| Number of pages | 18 |
| Journal | Pattern Recognition |
| Volume | 61 |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
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Palmprint recognition with Local Micro-structure Tetra Pattern. / Li, Gen; Kim, Jaihie.
In: Pattern Recognition, Vol. 61, 01.01.2017, p. 29-46.Research output: Contribution to journal › Article
TY - JOUR
T1 - Palmprint recognition with Local Micro-structure Tetra Pattern
AU - Li, Gen
AU - Kim, Jaihie
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Human palmprint-based biometric solutions have been studied extensively in both controlled and uncontrolled environments. However, the majority of existing methods do not reliably handle variations of translation, rotation, and blurriness of one's palm within the range of acceptable tolerance, which largely degrades the performance. Therefore, this paper presents a unique local descriptor called Local Micro-structure Tetra Pattern (LMTrP) and its application to palmprint recognition. The proposed descriptor takes advantage of local descriptors’ direction as well as thickness. In this paper, the palmprint image is first filtered with the line-shaped filter to effectively eliminate unnecessary features. Then, local region histograms of LMTrP are extracted and concatenated into one feature vector to represent the given image. Finally, the kernel linear discriminant analysis is applied on the feature vector for dimension reduction. The experimental results indicate that the proposed methods significantly outperform the state-of-the-art methods without the need to align the palmprint images.
AB - Human palmprint-based biometric solutions have been studied extensively in both controlled and uncontrolled environments. However, the majority of existing methods do not reliably handle variations of translation, rotation, and blurriness of one's palm within the range of acceptable tolerance, which largely degrades the performance. Therefore, this paper presents a unique local descriptor called Local Micro-structure Tetra Pattern (LMTrP) and its application to palmprint recognition. The proposed descriptor takes advantage of local descriptors’ direction as well as thickness. In this paper, the palmprint image is first filtered with the line-shaped filter to effectively eliminate unnecessary features. Then, local region histograms of LMTrP are extracted and concatenated into one feature vector to represent the given image. Finally, the kernel linear discriminant analysis is applied on the feature vector for dimension reduction. The experimental results indicate that the proposed methods significantly outperform the state-of-the-art methods without the need to align the palmprint images.
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UR - http://www.scopus.com/inward/citedby.url?scp=84988504405&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2016.06.025
DO - 10.1016/j.patcog.2016.06.025
M3 - Article
VL - 61
SP - 29
EP - 46
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
ER -