FaceNet recognition algorithm subject to multiple constraints: Assessment of the performance
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Scientific African
Abstract
Literature has it that the performance of most face recognition algorithms still decline in
multiple constrained environments (Occlusions and Expressions), despite the achieved successes
of deep learning face recognition algorithms. Using expression variant test face images syn thetically occluded at 30% and 40% rates, the study evaluated the performance of FaceNet
deep learning model for face recognition under the aforementioned constraints and when three
(3) statistical multiple imputation methods (Multivariable Imputation using Chain Equations
(MICE), MissForest and Regularized Expectation Maximization (RegEM)) are adopted for occlu sion recovery. Results of the study showed improved recognition rates of the study algorithm
when the imputation-based recovered faces were used for recognition compared with using
their multiple constrained counterparts. However, test faces reconstructed with the MissForest
imputation method were more accurately recognized using the FaceNet deep learning algorithm.
Furthermore, the study demonstrated that some simple augmentation schemes sufficed to
further enhance the performance of the FaceNet model. Specifically, the FaceNet algorithms
gave the highest average recognition rates (85.19% and 79.5% for 30% and 40% occlusion levels
respectively) under augmentation scheme IV (slight rotations, horizontal flipping, shearing,
brightness adjustments, and stretching) using MissForest as the de-occlusion mechanism. The
study also found that, no disparity existed in its performance with the choice of either Support
Vector Machines (SVM) or City Block (CB) for classification under augmentation scheme IV.
The study recommends using the MissForest imputation method in dealing with moderately
high occluded test faces with varying expressions to enhance the performance of the FaceNet
face recognition model.
Description
Research Article
Keywords
FaceNet algorithm, MICE, MissForest, RegEM