Image-Based Somatotype as a Biometric Trait for Non-Collaborative Person Recognition at a Distance and On-The-Move

Abstract

It has recently been shown in Re-Identification (Re-ID) work that full-body images of people reveal their somatotype, even after change in apparel. A significant advantage of this biometric trait is that it can easily be captured, even at a distance, as a full-body image of a person, taken by a standard 2D camera. In this work, full-body image-based somatotype is investigated as a novel soft biometric feature for person recognition at a distance and on-the-move. The two common scenarios of (i) identification and (ii) verification are both studied and evaluated. To this end, two different deep networks have been recruited, one for the identification and one for the verification scenario. Experiments have been conducted on popular, publicly available datasets and the results indicate that somatotype can indeed be a valuable biometric trait for identity recognition at a distance and on-the-move (and hence also suitable for non-collaborative individuals) due to the ease of obtaining the required images. This soft biometric trait can be especially useful under a wider biometric fusion scheme.

D4FLY Multimodal Biometric Database: multimodal fusion evaluation envisaging on-the-move biometric-based border control

“…Somatotype Somatotype defines a body type classification methodology and focusses on the measurement of the structural aspects of the human body. A person's somatotype can be determined from full-body images that can be captured at a distance on-the-move [3]. The somatotype dataset contains two static, and one on-the-move somatotype capture for each identity, giving three somatotype captures in total for each identity.…”

“…More specifically, static side captures were used as the gallery samples, while the on-the-move side captures were used as the probe samples. A Siamese deep learning network, optimized for extracting somatotype-related features [3], was implemented for evaluating the verification performance.…”