Published July 30, 2020 | Version v1
Journal article Open

Constrained Local Models (CLM) For Facial Feature Extraction using CLNF and SVR as Patch Experts

  • 1. Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, 22200 Besut, Terengganu, Malaysia.
  • 2. Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, 22200 Besut, Terengganu, Malaysia.
  • 1. Publisher

Description

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.

Files

A2766059120.pdf

Files (402.1 kB)

Name Size Download all
md5:b6aa108eee7d2c0778384b75fd13dd38
402.1 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2277-3878 (ISSN)

Subjects

ISSN
2277-3878
Retrieval Number
A2766059120/2020┬ęBEIESP