%0 Journal Article
%@ 02180014
%A Ali, A.S.O.
%A Asirvadam, V.S.
%A Malik, A.S.
%A Eltoukhy, M.M.
%A Aziz, A.
%D 2015
%F scholars:5798
%I World Scientific Publishing Co. Pte Ltd
%J International Journal of Pattern Recognition and Artificial Intelligence
%K Geometry; Gesture recognition; Sensitivity analysis, Classification accuracy; Face and gesture recognition; Face recognition methods; Face recognition systems; Facial recognition systems; Geometrical modeling; Mathematical relationship; Research networks, Face recognition
%N 5
%R 10.1142/S0218001415560066
%T Age-Invariant Face Recognition Using Triangle Geometric Features
%U https://khub.utp.edu.my/scholars/5798/
%V 29
%X Whilst facial recognition systems are vulnerable to different acquisition conditions, most notably lighting effects and pose variations, their particular level of sensitivity to facial aging effects 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 difference between a test image and a probe image. Consequently, developing an age-invariant platform continues to be a significant requirement for building an effective facial recognition system. The main objective of this work is to address the challenge of facial aging which affects 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 classification accuracy of more than 94. © 2015 World Scientific Publishing Company.
%Z cited By 6