Automatic Facial Feature Extraction for Face Recognition

Campadelli, Paola; Lanzarotti, Raffaella; Lipori, Giuseppe

doi:10.5772/4830

Cited by 24 publications

(19 citation statements)

References 45 publications

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“…From the image containing patches which depicts the face of a person, face components such as eyes, nose mouth etc. are localised with the help of with the help of feature extraction [4,5].…”

Section: Feature Extractionmentioning

confidence: 99%

A Review on Recognition Rate Techniques for 2-D and 3-D Image

Kaur¹,

Kaur²

2018

IJCA

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Machine recognition of faces is a biometric process in which face of a person is recognized by comparing the present image of a person with the image already present in the database. Demand is increasing rapidly as recognition is a vigorous research issue because of its non-copier characteristic. Compelling attention has been received by this technology because it has potential for tremendous applications like criminal identification, bank/store security, credit card verification, healthcare, marketing, automatic attendance etc. Face recognition is very secure method but its performance is degraded by some factors. Several researchers have recommended methods to nullify the effects of these factors. This paper provides a review on some effective 2D and 3D face images techniques with pose variations which are compared on the basis of recognition rates. From the discussed 2D face images techniques, recognition rate up to 100% was obtained by Kernal Canonical Correlation analysis (KCCA) only if input images are less than 200 images. If input images are more than 200 then 2D image based approach has higher recognition rate and is also simpler. From the discussed 3D techniques, recognition rate is highest of morphable model and also this technique is not affected by occlusion.

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Section: Feature Extractionmentioning

confidence: 99%

A Review on Recognition Rate Techniques for 2-D and 3-D Image

Kaur¹,

Kaur²

2018

IJCA

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“…The analysis of the human face is given by the description of its individual parts and their geometrical relationships [1] [2] [3].…”

Section: The Local Methodsmentioning

confidence: 99%

A new approach for Face Recognition Based on PCA & Double LDA Treatment combined with SVM

Hajraoui¹

2012

IOSRJEN

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In this paper we propose a faces recognition system. This system does not directly reproduce human vision on machine, but it seeks to find algorithms to achieve similar results by identifying a person using 2D image of his face. The descriptors used for features extraction, combine two algorithms: Principal Component Analysis (PCA) and a double Linear Discriminate Analysis (LDA) treatment. We chose the Support Vector Machine as an output classifier. Our approach has ensured a satisfactory recognition rate and a gain in terms of memory.

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“…mouth corners, eye centres, etc.). The latter is usually done by modelling texture information (Jesorsky et al, 2001;Vukadinovic and Pantic, 2005), shape information (Campadelli et al, 2005;Venkatesh at al., 2009), or combination of those (Campadelli et al, 2007;Cristinacce and Cootes, 2006). It has been shown (Lien et al, 2000;Tian et al, 2002;Tong et al, 2007) that facial expressions significantly deteriorate the performance of feature localization methods.…”

Section: Introductionmentioning

confidence: 99%