People re-identification is a fundamental operation for any multi-camera surveillance scenario. Until now, it has been performed by exploiting primarily appearance cues, hypothesizing that the individuals cannot change their clothes. In this paper, we relax this constraint by presenting a set of 3D soft-biometric cues, being insensitive to appearance variations, that are gathered using RGB-D technology. The joint use of these characteristics provides encouraging performances on a benchmark of 79 people, that have been captured in different days and with different clothing. This promotes a novel research direction for the re-identification community, supported also by the fact that a new brand of affordable RGB-D cameras have recently invaded the worldwide market.
Abstract-Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. Synthetic data represents a good compromise between realistic imagery, usually not required in re-identification since surveillance cameras capture low-resolution silhouettes, and complete control of the samples, which is useful in order to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, outperforms all competitors, matching subjects even with different apparel. The combination of synthetic data with Inception architectures opens up new research avenues in re-identification.
Transient biometrics, a new concept for biometric recognition, is introduced in this paper. A traditional perspective of biometric recognition systems concentrates on biometric characteristics that are as constant as possible (such as the eye retina), giving accuracy over time but at the same time resulting in resistance to their use for non-critical applications due to the possibility of misuse. In contrast, transient biometrics is based on biometric characteristics that do change over time aiming at increased acceptance in noncritical applications. We show that the fingernail is a transient biometric with a lifetime of approximately two months. Our evaluation datasets are available to the research community.
We present a novel closed-form solution for the joint self-calibration of video and range sensors. The approach single assumption is the availability of synchronous time of flight (i.e., range distances) measurements and visual position of the target on images acquired by a set of cameras. In such case, we make explicit a rank constraint that is valid for both image and range data. This rank property is used to find an initial and affine solution via bilinear factorization, which is then corrected by enforcing the metric constraints characteristic for both sensor modalities (i.e., camera and anchors constraints). The output of the algorithm is the identification of the target/range sensor position and the calibration of the cameras. The application extent of our approach is broad and versatile. In fact, with the same framework, we can deal with, but not restricted to, two very different applications. The first is aimed at calibrating cameras and microphones deployed in an unknown environment. The second uses a RGB-D device to reconstruct the 3D position of a set of keypoints using the camera and depth map images. Synthetic and real tests show the algorithm performance under different levels of noise and configurations of target locations, number of sensors and cameras.
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