Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Barnabas Victor

dblp:25/4769 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Biometric security
biometric fusion
0.012003
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics · IEEE Trans. Pattern Anal. Mach. Intell. 2003
Biometric security
biometric recognition
0.012003
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics · IEEE Trans. Pattern Anal. Mach. Intell. 2003
Biometric security
ear biometrics
0.012003
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics · IEEE Trans. Pattern Anal. Mach. Intell. 2003
Biometric security
face recognition
0.012003
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
YearPublicationVenuePosition
2003 Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
abstract
Researchers 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