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Making the most of single sensor information : a novel fusion approach for 3D face recognition using region covariance descriptors and Gaussian mixture models
ID
Križaj, Janez
(
Avtor
),
ID
Dobrišek, Simon
(
Avtor
),
ID
Štruc, Vitomir
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(1,55 MB)
MD5: A4DDBE58E8709D398DB6DCF0FEAC9E89
URL - Izvorni URL, za dostop obiščite
https://www.mdpi.com/1424-8220/22/6/2388
Galerija slik
Izvleček
Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process suffer. In this paper, we focus on face recognition using images captured by a single 3D sensor and propose a method based on the use of region covariance matrixes and Gaussian mixture models (GMMs). All steps of the proposed framework are automated, and no metadata, such as pre-annotated eye, nose, or mouth positions is required, while only a very simple clustering-based face detection is performed. The framework computes a set of region covariance descriptors from local regions of different face image representations and then uses the unscented transform to derive low-dimensional feature vectors, which are finally modeled by GMMs. In the last step, a support vector machine classification scheme is used to make a decision about the identity of the input 3D facial image. The proposed framework has several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrixes), the ability to explore facial images at different levels of locality, and the ability to integrate a domain-specific prior knowledge into the modeling procedure. Several normalization techniques are incorporated into the proposed framework to further improve performance. Extensive experiments are performed on three prominent databases (FRGC v2, CASIA, and UMB-DB) yielding competitive results.
Jezik:
Angleški jezik
Ključne besede:
biometrics
,
computer vision
,
3D face recognition
,
fusion
,
covariance descriptors
,
face recognition
,
3D images
,
local descriptors
,
statistical models
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FE - Fakulteta za elektrotehniko
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2022
Št. strani:
26 str.
Številčenje:
Vol. 22, iss. 6, art. 2388
PID:
20.500.12556/RUL-137514
UDK:
004.93:57.087.1
ISSN pri članku:
1424-8220
DOI:
10.3390/s22062388
COBISS.SI-ID:
101606403
Datum objave v RUL:
20.06.2022
Število ogledov:
687
Število prenosov:
100
Metapodatki:
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Objavi na:
Gradivo je del revije
Naslov:
Sensors
Skrajšan naslov:
Sensors
Založnik:
MDPI
ISSN:
1424-8220
COBISS.SI-ID:
10176278
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:
20.03.2022
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
biometrija
,
računalniški vid
,
3D razpoznavanje obrazov
,
fuzija
,
kovariančni opisniki
Projekti
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P2-0250
Naslov:
Metrologija in biometrični sistemi
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