Título: Soft Biometrics Classification Using Denoising Convolutional Autoencoders and Support Vector Machines
Autores: Romero, Nelson;Gutoski, Matheus;Hattori, Leandro;Lopes, Heitor
This work presents a methodology to perform the classification of soft biometrics in images of pedestrians using a Denoising Convolutional Autoencoder as feature extractor and a Support Vector Machine as classifier. The Denoising Convolutional Autoencoder was trained with a custom dataset containing a combination of five available datasets (3DPES, Market1501, PRID2011, VIPeR and ETHZ) and used as a feature extractor of the images of the VIPeR dataset. The extracted features were then used as input values for a Support Vector Machine classifier, with its hyper-parameters set by using Grid Search, in order to classify the images according to two soft biometrics or labels: Long-Hair and Sunglasses. The results obtained with the proposed approach were compared to those obtained using other well-known feature extractor: Histogram of Oriented Gradients.
Deep Learning;Convolutional Neural Networks;Data Augmentation;Unbalanced Datasets
Código DOI: 10.21528/CBIC2017-78
Artigo em pdf: cbic-paper-78.pdf
Arquivo BibTeX: cbic-paper-78.bib