Soft-biometrics : unconstrained authentication in a surveillance environment

Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. These include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics (i.e. face, voice) which require cooperation from the subject, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. Whilst these traits cannot provide robust authentication, they can be used to provide coarse authentication or identification at long range, locate a subject who has been previously seen or who matches a description, as well as aid in object tracking. In this paper we propose three part (head, torso, legs) height and colour soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database. We show that these models, whilst not as accurate as traditional biometrics, can still achieve acceptable rates of accuracy in situations where traditional biometrics cannot be applied.

Text segmentation in unconstrained hand-drawings in whiteboard photos

In this paper we present a robust method to detect handwritten text from unconstrained drawings on normal whiteboards. Unlike printed text on documents, free form handwritten text has no pattern in terms of size, orientation and font and it is often mixed with other drawings such as lines and shapes. Unlike handwritings on paper, handwritings on a normal whiteboard cannot be scanned so the detection has to be based on photos. Our work traces straight edges on photos of the whiteboard and builds graph representation of connected components. We use geometric properties such as edge density, graph density, aspect ratio and neighborhood similarity to differentiate handwritten text from other drawings. The experiment results show that our method achieves satisfactory precision and recall. Furthermore, the method is robust and efficient enough to be deployed in a mobile device. This is an important enabler of business applications that support whiteboard-centric visual meetings in enterprise scenarios. © 2012 IEEE.

Structural characterization of backbone-expanded helices in hybrid peptides: (αγ) n and (αβ) n sequences with unconstrained β and γ homologues of L-Val

Learning your αβγ's: The diversity of hydrogen-bonding patterns in backbone-expanded hybrid helices is shown by crystal-structure determination of several oligomeric peptides (see scheme; C=gray; H=white; O=red; N=blue). C 12 helices were observed in the αγ peptide series for n=2-8. In comparison, the αα peptide and αβ peptide sequences show C 10 and mixed C 14/C 15 helices, respectively. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Unconstrained Homooligomeric gamma-Peptides Show High Propensity for C-14 Helix Formation

Monosubstituted gamma(4)-residues (gamma(4)Leu, gamma(4)Ile, and gamma(4)Val) form helices even in short homooligomeric sequences. C-14 helix formation is established by X-ray diffraction in homooligomeric (gamma)(n) tetra-, hexa- and decapeptide sequences demonstrating the high propensity of gamma residues, with proteinogenic side chains, to adopt locally folded conformations.

C-12 Helices in Long Hybrid (alpha gamma)(n) Peptides Composed Entirely of Unconstrained Residues with Proteinogenic Side Chains

Unconstrained gamma(4) amino acid residues derived by homologation of proteinogenic amino acids facilitate helical folding in hybrid (alpha gamma)(n) sequences. The C-12 helical conformation for the decapeptide, Boc-Leu-gamma(4)(R)Val](5)-OMe, is established in crystals by X-ray diffraction. A regular C-12 helix is demonstrated by NMR studies of the 18 residue peptide, Boc-Leu-gamma(4)(AR)Val](9)-OMe, and a designed 16 residue (alpha gamma)(n) peptide, incorporating variable side chains. Unconstrained (alpha gamma)(n) peptides show an unexpectedly high propensity for helical folding in long polypeptide sequences.

Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts. © 2013 IEEE.