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

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.