Exploring automatic extraction of body-based soft biometrics

Abstract

Given the growing interest in soft biometrics and its application in many areas related to biometrics, this paper focuses on the automatic extraction of body-based soft biometric attributes from single-shot images. The selected body soft biometrics are: height, shoulder width, hips width, arms length, body complexion and hair colour. For the extraction of these attributes, the Southampton Multi-Biometric Tunnel Database has been used with a total of 222 subjects. Images at far distance between the subject and the camera were considered in order to be able to extract the whole body of the person. Feature extraction is based on distances between key points automatically extracted from the person's silhouette, and also based on pixel information. Support Vector Machines (SVM) are used as the matchers, achieving promising results. Finally, given an image of a person at a distance, the system automatically gives the probability for the classes of each body-based soft biometrics considered, which could be seen as a description of the subject's body. This description could be used to reduce the search space in forensic applications, or to improve the robustness of biometric recognition systems at a distance, especially for face and gait systems, among other applications.