FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP
FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP
12 GLOSSARY
13Chapter 1IntroductionTraditional biometrics offer a natural and reliable solution for establishing the identity of anindividual, and for this reason, the use of human physical and behavioral characteristics has beenincreasingly adopted in security applications. With this approach maintaining various advantagessuch as universality, robustness, permanence and accessibility, it is not surprising that currentintrusion detection and security mechanisms and systems include by default at least one biometrictrait.Building on this progress, the latest addition of soft biometrics builds and adds on the mainadvantages of classical biometrics.The beginnings of soft biometric science were laid by Alphonse Bertillon in the 19th century,who firstly introduced the idea of a person identification system based on biometric, morphologicaland anthropometric determinations, see [Rho56]. In his effort, Bertillon considered traits likecolors of eye, hair, beard and skin; shape and size of the head, as well as general discriminators likeheight or weight and also indelible marks such as birth marks, scars or tattoos. These descriptorsmainly comprise what is now referred to as the family of soft biometrics, a term first introducedby Jain et al. [JDN04b] to describe the set of characteristics that provide (some) information aboutan individual, but that are not generally sufficient for fully describing and identifying a person,mainly due to the lack of distinctiveness and permanence of such traits. As stated later [JDN04a],such soft biometrics traits can be inexpensive to compute, can be sensed at a distance, do notrequire the cooperation of the surveillance subjects, and can be efficiently used to narrow downa search for an individual from a large set of people. Along the lines of semantic annotation([SGN08] and [RN10]) we here note the human compliance of soft biometrics as a main differencebetween soft biometrics and classical biometrics - a difference that renders soft biometrics suitablefor many applications. The terms light biometrics see in [ALMV04], similes see in [KBBN09]and attributes see in [VFT + 09] have been describing traits we associate to soft biometrics. Thefollowing definition clarifies what is considered here as soft-biometric traits.Definition: Soft biometric traits are physical, behavioral or adhered human characteristics,classifiable in pre–defined human compliant categories. These categories are, unlike in the classicalbiometric case, established and time–proven by human experience with the aim of differentiatingindividuals. In other words soft biometric traits are created in a natural way, used by peopleto characterize other people.Our interest in this thesis is in understanding the role that soft biometrics can play in securityand commercial systems of the future. In brief we begin by specifying soft biometric traits thatadhere to the above definition. After an overview of related work, we proceed to explore differentapplications that benefit from soft biometric systems (SBSs), focusing on surveillance relatedperson identification, and on pruning of large surveillance related searches. We also consider the
- Page 1: FACIAL SOFT BIOMETRICSMETHODS, APPL
- Page 5: AcknowledgementsThis thesis would n
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- Page 11 and 12: 97 Practical implementation of soft
- Page 13: 11Notations used in this workE : st
- Page 17 and 18: 15event of collision, which is of s
- Page 19 and 20: 17ric. In Section 6.6 we employ the
- Page 21 and 22: 19Chapter 2Soft biometrics: charact
- Page 23 and 24: 21is the fusion of soft biometrics
- Page 25 and 26: 23plied on low resolution grey scal
- Page 27 and 28: 25Chapter 3Bag of facial soft biome
- Page 29 and 30: 27In this setting we clearly assign
- Page 31 and 32: 29Table 3.1: SBSs with symmetric tr
- Page 33 and 34: 31corresponding to p(n,ρ). Towards
- Page 35 and 36: the same category (all subjects in
- Page 37 and 38: 3.5.2 Analysis of interference patt
- Page 39 and 40: an SBS by increasing ρ, then what
- Page 41 and 42: 39Table 3.4: Example for a heuristi
- Page 43 and 44: 41for a given randomly chosen authe
- Page 45 and 46: 43Chapter 4Search pruning in video
- Page 47 and 48: 45Figure 4.1: System overview.SBS m
- Page 49 and 50: 472.52rate of decay of P(τ)1.510.5
- Page 51 and 52: 49to be the probability that the al
- Page 53 and 54: 51The following lemma describes the
- Page 55 and 56: 534.5.1 Typical behavior: average g
- Page 57 and 58: 55n = 50 subjects, out of which we
- Page 59 and 60: 5710.950.9pruning Gain r(vt)0.850.8
- Page 61 and 62: 59for one person, for trait t, t =
- Page 63 and 64: 61Chapter 5Frontal-to-side person r
12 GLOSSARY