Demo Data from SIGIR 2026 / reviewers in the wild / expert
Barnabas Victor
dblp:25/4769
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Biometric security
biometric fusion |
0.0 | 1 | 2003 | Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics · IEEE Trans. Pattern Anal. Mach. Intell. 2003 |
Biometric security
biometric recognition |
0.0 | 1 | 2003 | Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics · IEEE Trans. Pattern Anal. Mach. Intell. 2003 |
Biometric security
ear biometrics |
0.0 | 1 | 2003 | Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics · IEEE Trans. Pattern Anal. Mach. Intell. 2003 |
Biometric security
face recognition |
0.0 | 1 | 2003 | Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics · IEEE Trans. Pattern Anal. Mach. Intell. 2003 |
Methods — techniques the papers use, named apart from their topics
principal component analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Comparison and Combination of Ear and Face Images in Appearance-Based BiometricsabstractResearchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent, respectively, in one experiment. We also find that multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric, for example, 90.9 percent in the analogous experiment. Kyong I. Chang, Kevin W. Bowyer, Sudeep Sarkar, Barnabas Victor |
IEEE Trans. Pattern Anal. Mach. Intell. | 4 |