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IMPROVING IMAGE CLASSIFICATION WITH FREQUENCY DOMAIN LAYERS FOR FEATURE EXTRACTION

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Stuchi, Jose A. ; Angeloni, Marcus A. ; Pereira, Rodrigo F. ; Boccato, Levy ; Folego, Guilherme ; Prado, Paulo V. S. ; Attux, Romis R. F. ; Ueda, N ; Watanabe, S ; Matsui, T ; Chien, JT ; Larsen, J
Número total de Autores: 12
Tipo de documento: Artigo Científico
Fonte: 2017 IEEE 27TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING; v. N/A, p. 6-pg., 2017-01-01.
Resumo

Machine learning has been increasingly used in current days. Great improvements, especially in deep neural networks, helped to boost the achievable performance in computer vision and signal processing applications. Although different techniques were applied for deep architectures, the frequency domain has not been thoroughly explored in this field. In this context, this paper presents a new method for extracting discriminative features according to the Fourier analysis. The proposed frequency extractor layer can be combined with deep architectures in order to improve image classification. Computational experiments were performed on face liveness detection problem, yielding better results than those presented in the literature for the grandtest protocol of Replay-Attack Database. This paper also aims to raise the discussion on how frequency domain layers can be used in deep architectures to further improve the network performance. (AU)

Processo FAPESP: 16/00985-5 - Equipamento portátil para diagnóstico em retina controlado por smartphone
Beneficiário:Flávio Pascoal Vieira
Modalidade de apoio: Auxílio à Pesquisa - Pesquisa Inovativa em Pequenas Empresas - PIPE