Analysis of Wavelet Families for Face Recognition

Ferreira, Fabrcio Paes; Carvalho, Tiago Buarque Assunção de

doi:10.1109/wvc.2017.00023

Cited by 2 publications

(1 citation statement)

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“…Finally, we verify if the obtained results are significant, according to a 95% confidence level hypothesis test. A confidence interval approach can be used (Ferreira et al (2017)). It consists of calculating the confidence interval at 95% of the difference of the average accuracy rates of two different classifiers: • if this 95% confidence interval does not contain 0 as a point, then we will conclude that the two average accuracy rates are different, • if this confidence interval contains 0, then we will conclude that there is nothing to confirm that the two means are different.…”

Section: Resultsmentioning

confidence: 99%

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

Boussaad

Boucetta

2021

International Journal of Applied Metaheuristic Computing

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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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Section: Resultsmentioning

confidence: 99%