Abstract: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… Show more
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