Constrained local models (CLM) for facial feature extraction using CLNF and SVR as patch experts

Fatma Susilawati, Mohamad and Ayah, Alsarayreh (2020) Constrained local models (CLM) for facial feature extraction using CLNF and SVR as patch experts. International Journal of Recent Technology and Engineering, 9 (2). pp. 40-43. ISSN 2277-3878

[img] Text
Restricted to Registered users only

Download (402kB)


Methods for detection of facial characteristics have again developed greatly in recent times. However, they also argue in the presence of poor lighting conditions for amazing pose or occlusions. A well-established group of strategies for facial feature extraction is the Constrained Local Model (CLM). Recently, they are bringing cascaded regression-built methodologies out of favor. This is because the failure of presenting nearby CLM detectors to model the highly complex special signature look affected to a small degree by voice, illumination, facial hair and make-up. This paper keeps tabs on execution to collect facial features for the Constrained Local Model (CLM). CLM model relies on patch model to collect facial image demand features. In this paper patch model built using Support Vector Regression (SVR) and Constrained Local Neural Field (CLNF). We show that the CLNF model exceeds SVR by a large margin on the LFPW database to identify facial landmarks.

Item Type: Article
Uncontrolled Keywords: Features Extraction, CLNF, SVR, CLM.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Informatics & Computing
Depositing User: Fatin Safura
Date Deposited: 17 Apr 2022 08:02
Last Modified: 24 Apr 2022 01:59

Actions (login required)

View Item View Item