Deep Learning for Age Estimation Using EfficientNet

Aruleba, Idowu; Viriri, Serestina

doi:10.1007/978-3-030-85030-2_34

Cited by 9 publications

(1 citation statement)

References 19 publications

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“…A comparison of the results obtained from our emotion and age recognition model on the AffectNet and Adience datasets with well-known scientific works is presented. AffectNet includes faces images with different emotions, while Adience contains faces images of different ages and genders [7]. Compared to existing models, the authors in [8] proposed a model called Deep Squeeze-and-Excitation Network (DSENet) for facial emotion recognition using the SE-Block method and the combination of elements from three architectures: DenseNet, MobileNet, and SE-Inception-ResNet-v1.…”

Section: Discussion and Experimental Resultsmentioning

confidence: 99%

Development of an emotion and age recognition model based on the combination of local binary patterns and squeeze-and-excitation block method

Soma,

Kopanitsa

2024

Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024)

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This paper presents models for improving accuracy and efficiency in recognizing a person's emotional state and age based on facial expressions using the combination of Local Binary Patterns and Squeeze-and-Excitation Block methods. The proposed models were trained on the AffectNet dataset for emotion recognition and the Adience dataset for age recognition with four classes [angry, happy, neutral, sad] and [6-20, 25-30, 42-48, 60-98]. The accuracy and speed of these models, achieved after training, were compared with the results described in scientific articles published in the Scopus database. According to the obtained experimental data, the first and second developed models achieved validation accuracy of 81.5% and 72.3%, respectively.

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Section: Discussion and Experimental Resultsmentioning

confidence: 99%