Age-Invariant Face Recognition Using Triangle Geometric Features

Osman Ali, Amal Seralkhatem and Asirvadam , Vijanth Sagayan and Malik, Aamir Saeed and Eltoukhy , Mohamed Meselhy and Abd Aziz, Azrina (2015) Age-Invariant Face Recognition Using Triangle Geometric Features. International Journal of Pattern Recognition and Artificial Intelligence. ISSN 2180014

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Whilst facial recognition systems are vulnerable to di®erent acquisition conditions, most
notably lighting e®ects and pose variations, their particular level of sensitivity to facial aging
e®ects is yet to be researched. The face recognition vendor test (FRVT) 2012's annual
statement estimated deterioration in the performance of face recognition systems due to
facial aging. There was about 5% degradation in the accuracies of the face recognition systems
for each single year age di®erence between a test image and a probe image. Consequently,
developing an age-invariant platform continues to be a signi¯cant requirement for
building an e®ective facial recognition system. The main objective of this work is to address
the challenge of facial aging which a®ects the performance of facial recognition systems.
Accordingly, this work presents a geometrical model that is based on extracting a number of
triangular facial features. The proposed model comprises a total of six triangular areas
connecting and surrounding the main facial features (i.e. eyes, nose and mouth). Furthermore,
a set of thirty mathematical relationships are developed and used for building a feature
vector for each sample image. The areas and perimeters of the extracted triangular areas are
calculated and used as inputs for the developed mathematical relationships. The performance
of the system is evaluated over the publicly available face and gesture recognition research
network (FG-NET) face aging database. The performance of the system is compared with
that of some of the state-of-the-art face recognition methods and state-of-the-art age-invariant
face recognition systems. Our proposed system yielded a good performance in term of
classi¯cation accuracy of more than 94%.

Item Type: Article
Impact Factor: 0.669
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Departments / MOR / COE: Departments > Electrical & Electronic Engineering
Research Institutes > Institute for Health Analytics
Depositing User: Dr Aamir Saeed Malik
Date Deposited: 07 Oct 2016 01:42
Last Modified: 07 Oct 2016 01:42

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