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Bala N, Gupta R, Kumar A. An Effective Multimodal Biometric System Based on Textural Feature Descriptor. PATTERN RECOGNITION AND IMAGE ANALYSIS 2022. [DOI: 10.1134/s1054661822030063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Boussaad L, Boucetta A. Extreme Learning Machine-Based Age-Invariant Face Recognition With Deep Convolutional Descriptors. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2022. [DOI: 10.4018/ijamc.290540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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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Affiliation(s)
- Leila Boussaad
- Computer Science Department, Batna 2 University, Algeria
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