Open AccessOptimization of deep learning features for age-invariant face recognition
Author(s) -
Amal A. Moustafa,
Ahmed Elnakib,
Nihal F. F. Areed
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i2.pp1833-1841
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , preprocessor , facial recognition system , deep learning , k nearest neighbors algorithm , classifier (uml) , euclidean distance , invariant (physics) , face (sociological concept) , mathematics , social science , sociology , mathematical physics
This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.