Extreme Learning Machine-Based Age-Invariant Face Recognition With Deep Convolutional Descriptors

Extreme Learning Machine-Based Age-Invariant Face Recognition With Deep Convolutional Descriptors

Leila Boussaad, Aldjia Boucetta
Copyright: © 2022 |Pages: 18
DOI: 10.4018/IJAMC.290540
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Abstract

The principal intention of this paper is to study face recognition across age progression at two levels: feature extraction and classification. In other words, this work aims to prove the benefit of replacing the Softmax layer of the Deep-Convolutional Neural Networks (CNN) by Extreme Learning Machine (ELM) classifier based on deep features computed from fully-connected layer of pre-trained AlexNet CNN model, in a context of age-invariant face recognition. Experimental results indicate that the ELM classifier combined with feature extracted by the pre-trained AlexNet CNN model worked effectively for face recognition across age progression. As significant highest mean accuracy rates are always obtained using ELM classifier. These results are more significant, following a 95% confidence level hypothesis test.
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The methods that have been proposed in regards to the aging effects on face recognition can be categorized in two main classes (Ramanathan et al. (2009a)): “generative”, and “discriminative” methods.

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