Real-Time Masked Face Recognition Using FaceNet and Supervised Machine Learning

Adhinata, Faisal Dharma; Tanjung, Nia Annisa Ferani; Widayat, Wahyu; Pasfica, Gracia Rizka; Satura, Fadlan Raka

doi:10.1007/978-981-19-1804-9_15

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…To achieve this goal, we studied the sensitivity to a dataset imbalance of the following contemporary neural networks: Xception, ViT-384 [32], ViT-224, VGG19, ResNet34 [33], ResNet50, ResNet101 [34], Inception_v3, DenseNet201 [35], DenseNet161 [36], and DeIT [37]. Different imbalance reduction techniques and their ensembles were used to determine this sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Modern Deep Learning Neural Networks to Unbalanced Datasets in Multiclass Classification Problems

et al. 2023

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One of the critical problems in multiclass classification tasks is the imbalance of the dataset. This is especially true when using contemporary pre-trained neural networks, where the last layers of the neural network are retrained. Therefore, large datasets with highly unbalanced classes are not good for models’ training since the use of such a dataset leads to overfitting and, accordingly, poor metrics on test and validation datasets. In this paper, the sensitivity to a dataset imbalance of Xception, ViT-384, ViT-224, VGG19, ResNet34, ResNet50, ResNet101, Inception_v3, DenseNet201, DenseNet161, DeIT was studied using a highly imbalanced dataset of 20,971 images sorted into 7 classes. It is shown that the best metrics were obtained when using a cropped dataset with augmentation of missing images in classes up to 15% of the initial number. So, the metrics can be increased by 2–6% compared to the metrics of the models on the initial unbalanced data set. Moreover, the metrics of the rare classes’ classification also improved significantly–the True Positive value can be increased by 0.3 or more. As a result, the best approach to train considered networks on an initially unbalanced dataset was formulated.

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