Demo Data from VLDB 2026 / reviewers in the wild / expert
Gholamreza Amayeh
dblp:52/785
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Biometric security · 100% | |
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Biometric security › hand biometrics
hand authentication |
0.1 | 1 | 2007 | A Component-Based Approach to Hand Verification · CVPR 2007 |
Image and video processing
image segmentation |
0.0 | 1 | 2007 | A Component-Based Approach to Hand Verification · CVPR 2007 |
Methods — techniques the papers use, named apart from their topics
zernike moments · 0.1morphological operators · 0.1information fusion · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Hand-based verification and identification using palm-finger segmentation and fusion
Gholamreza Amayeh, George Bebis, Ali Erol, Mircea Nicolescu |
Comput. Vis. Image Underst. | 1 |
| 2007 | A Component-Based Approach to Hand VerificationabstractThis paper describes a novel hand-based verification system based on palm-finger segmentation and fusion. The proposed system operates on 2D hand images acquired by placing the hand on a planar lighting table without any guidance pegs. The segmentation of the palm and the fingers is performed without requiring the extraction of any landmark points on the hand. First, the hand is segmented from the forearm using a robust, iterative methodology based on morphological operators. Then, the hand is segmented into six regions corresponding to the palm and the fingers using morphological operators again. The geometry of each component of the hand is represented using high order Zernike moments which are computed using an efficient methodology. Finally, verification is performed by fusing information from different parts of the hand. The proposed system has been evaluated on a database of 101 subjects illustrating high accuracy and robustness. Comparisons with competitive approaches that use the whole hand illustrate the superiority of the proposed, component-based, approach both in terms of accuracy and robustness. Qualitative comparisons with state of the art systems illustrate that the proposed system has comparable or better performance. Gholamreza Amayeh, George Bebis, Ali Erol, Mircea Nicolescu |
CVPR | 1 |