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
90 7. PRACTICAL IMPLEMENTATION OF SOFT BIOMETRICS CLASSIFICATION ALGORITHMSFigure 7.8: Eye colors of left and right eyes for 2 subjects (for 4 different illuminations).7.4.4 Camera sensorsFor the sake of completeness we proceed to provide a graph on the shift between two camerasensors (Logitech Webcam and Cannon 400D). The measured color data is clearly influenced bythe characteristics of the cameras.Figure 7.9: Eye colors captured with two camera sensors (constant illumination).We note that the presented study identifies each one of the examined influential factors asdisturbing for eye color categorization. The measure of importance for each one of them is ascertainedby the embedding application.
917.5 SummaryThis chapter presented classification algorithms related to six facial soft biometric traits, namelythe color of eye, skin and hair and moreover beard, moustache and glasses and provide accordingresults. We then specifically focused on and examined eye color, developed an automatic eyeclassification system and studied the impact of external factors on the appearance of eye color. Wehave identified and illustrated color shifts due to variation of illumination, presence of glasses, thedifference of perception of left and right eye, as well as due to having two different camera sensors.In the last chapter 8 we deviate from the analysis and development point of view towards SBSand examine instead the user friendliness of such a system by providing a study on user acceptancetowards such systems. In this study we compare SBSs to other biometric systems, as well as tothe classical PIN system toward access control.
- 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
- Page 65 and 66: 63Figure 5.1: Frontal / gallery and
- Page 67 and 68: 6510.90.80.7Skin colorHair colorShi
- Page 69 and 70: 6710.90.80.70.6Perr0.50.40.30.20.10
- Page 71 and 72: 69Chapter 6Soft biometrics for quan
- Page 73 and 74: 71raphy considerations include [BSS
- Page 75 and 76: 73Figure 6.3: Example image of the
- Page 77 and 78: 75A direct way to find a relationsh
- Page 79 and 80: 77- Pearson’s correlation coeffic
- Page 81 and 82: 79shown to have a high impact on ou
- Page 83 and 84: 81Chapter 7Practical implementation
- Page 85 and 86: 834) Eye glasses detection: Towards
- Page 87 and 88: 857.2 Eye color as a soft biometric
- Page 89 and 90: 87Table 7.5: GMM eye color results
- Page 91: 89and office lights, daylight, flas
- Page 95 and 96: 93Chapter 8User acceptance study re
- Page 97 and 98: 95Table 8.1: User experience on acc
- Page 99 and 100: 97scared of their PIN being spying.
- Page 101 and 102: 99Table 8.2: Comparison of existing
- Page 103 and 104: 101ConclusionsThis dissertation exp
- Page 105 and 106: 103Future WorkIt is becoming appare
- Page 107 and 108: 105Appendix AAppendix for Section 3
- Page 109 and 110: 107- We are now left withN −F = 2
- Page 111 and 112: 109Appendix BAppendix to Section 4B
- Page 113 and 114: 111Blue Green Brown BlackBlue 0.75
- Page 115 and 116: 113Appendix CAppendix for Section 6
- Page 117 and 118: 115Appendix DPublicationsThe featur
- Page 119 and 120: 117Bibliography[AAR04] S. Agarwal,
- Page 121 and 122: 119[FCB08] L. Franssen, J. E. Coppe
- Page 123 and 124: 121[Ley96] M. Leyton. The architect
- Page 125 and 126: 123[RN11] D. Reid and M. Nixon. Usi
- Page 127 and 128: 125[ZG09] X. Zhang and Y. Gao. Face
- Page 129: 2Rapporteurs:Prof. Dr. Abdenour HAD
- Page 132 and 133: Biométrie faciale douce 2Les terme
- Page 134 and 135: Biométrie faciale douce 4une perso
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- Page 138 and 139: Biométrie faciale douce 8Nous nous
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90 7. PRACTICAL IMPLEMENTATION OF <strong>SOFT</strong> <strong>BIOMETRICS</strong> CLASSIFICATION ALGORITHMSFigure 7.8: Eye colors <strong>of</strong> left and right eyes for 2 subjects (for 4 different illuminations).7.4.4 Camera sensorsFor the sake <strong>of</strong> completeness we proceed to provide a graph on the shift between two camerasensors (Logitech Webcam and Cannon 400D). The measured color data is clearly influenced bythe characteristics <strong>of</strong> the cameras.Figure 7.9: Eye colors captured with two camera sensors (constant illumination).We note that the presented study identifies each one <strong>of</strong> the examined influential factors asdisturbing for eye color categorization. The measure <strong>of</strong> importance for each one <strong>of</strong> them is ascertainedby the embedding application.
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