Extracting human attributes using a convolutional neural network approach

Extracting high level information from digital images and videos is a hard problem frequently faced by the computer vision and machine learning communities. Modern surveillance systems can monitor people, cars or objects by using computer vision methods. The objective of this work is to propose a method for identifying soft biometrics, in the form of clothing and gender, from images containing people, as a previous step for further identifying people themselves. We propose a solution to this classification problem using a Convolutional Neural Network, working as an all-in-one feature extractor and classifier. This method allows the development of a high-level end-to-end clothing/gender classifier. Experiments were done comparing the CNN with hand-designed classifiers. Also, two different operating modes of CNN are proposed and coin pared each other. The results obtained were very promising, showing that is possible to extract soft-biometrics attributes using an end-to-end CNN classifier. The proposed method achieved a good generalization capability, classifying the three different attributes with good accuracy. This suggests the possibility to search images using soft biometrics as search terms. (C) 2015 Elsevier B.V. All rights reserved.