---
_id: '53356'
author:
- first_name: Philipp
full_name: Terhörst, Philipp
id: '97123'
last_name: Terhörst
- first_name: Marco
full_name: Huber, Marco
last_name: Huber
- first_name: Naser
full_name: Damer, Naser
last_name: Damer
- first_name: Florian
full_name: Kirchbuchner, Florian
last_name: Kirchbuchner
- first_name: Kiran
full_name: Raja, Kiran
last_name: Raja
- first_name: Arjan
full_name: Kuijper, Arjan
last_name: Kuijper
citation:
ama: Terhörst P, Huber M, Damer N, Kirchbuchner F, Raja K, Kuijper A. Pixel-Level
Face Image Quality Assessment for Explainable Face Recognition. IEEE Transactions
on Biometrics, Behavior, and Identity Science. 2023;5(2):288-297. doi:10.1109/tbiom.2023.3263186
apa: Terhörst, P., Huber, M., Damer, N., Kirchbuchner, F., Raja, K., & Kuijper,
A. (2023). Pixel-Level Face Image Quality Assessment for Explainable Face Recognition.
IEEE Transactions on Biometrics, Behavior, and Identity Science, 5(2),
288–297. https://doi.org/10.1109/tbiom.2023.3263186
bibtex: '@article{Terhörst_Huber_Damer_Kirchbuchner_Raja_Kuijper_2023, title={Pixel-Level
Face Image Quality Assessment for Explainable Face Recognition}, volume={5}, DOI={10.1109/tbiom.2023.3263186},
number={2}, journal={IEEE Transactions on Biometrics, Behavior, and Identity Science},
publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Terhörst,
Philipp and Huber, Marco and Damer, Naser and Kirchbuchner, Florian and Raja,
Kiran and Kuijper, Arjan}, year={2023}, pages={288–297} }'
chicago: 'Terhörst, Philipp, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran
Raja, and Arjan Kuijper. “Pixel-Level Face Image Quality Assessment for Explainable
Face Recognition.” IEEE Transactions on Biometrics, Behavior, and Identity
Science 5, no. 2 (2023): 288–97. https://doi.org/10.1109/tbiom.2023.3263186.'
ieee: 'P. Terhörst, M. Huber, N. Damer, F. Kirchbuchner, K. Raja, and A. Kuijper,
“Pixel-Level Face Image Quality Assessment for Explainable Face Recognition,”
IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 5,
no. 2, pp. 288–297, 2023, doi: 10.1109/tbiom.2023.3263186.'
mla: Terhörst, Philipp, et al. “Pixel-Level Face Image Quality Assessment for Explainable
Face Recognition.” IEEE Transactions on Biometrics, Behavior, and Identity
Science, vol. 5, no. 2, Institute of Electrical and Electronics Engineers
(IEEE), 2023, pp. 288–97, doi:10.1109/tbiom.2023.3263186.
short: P. Terhörst, M. Huber, N. Damer, F. Kirchbuchner, K. Raja, A. Kuijper, IEEE
Transactions on Biometrics, Behavior, and Identity Science 5 (2023) 288–297.
date_created: 2024-04-08T09:33:24Z
date_updated: 2024-08-21T07:07:35Z
doi: 10.1109/tbiom.2023.3263186
intvolume: ' 5'
issue: '2'
keyword:
- Artificial Intelligence
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Instrumentation
language:
- iso: eng
page: 288-297
publication: IEEE Transactions on Biometrics, Behavior, and Identity Science
publication_identifier:
issn:
- 2637-6407
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
status: public
title: Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
type: journal_article
user_id: '97123'
volume: 5
year: '2023'
...
---
_id: '27506'
abstract:
- lang: eng
text: Explainability for machine learning gets more and more important in high-stakes
decisions like real estate appraisal. While traditional hedonic house pricing
models are fed with hard information based on housing attributes, recently also
soft information has been incorporated to increase the predictive performance.
This soft information can be extracted from image data by complex models like
Convolutional Neural Networks (CNNs). However, these are intransparent which excludes
their use for high-stakes financial decisions. To overcome this limitation, we
examine if a two-stage modeling approach can provide explainability. We combine
visual interpretability by Regression Activation Maps (RAM) for the CNN and a
linear regression for the overall prediction. Our experiments are based on 62.000
family homes in Philadelphia and the results indicate that the CNN learns aspects
related to vegetation and quality aspects of the house from exterior images, improving
the predictive accuracy of real estate appraisal by up to 5.4%.
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
citation:
ama: 'Kucklick J-P. Visual Interpretability of Image-based Real Estate Appraisal.
In: 55th Annual Hawaii International Conference on System Sciences (HICSS-55).
; 2022.'
apa: Kucklick, J.-P. (2022). Visual Interpretability of Image-based Real Estate
Appraisal. 55th Annual Hawaii International Conference on System Sciences (HICSS-55).
Hawaii International Conference on System Science (HICSS), Virtual.
bibtex: '@inproceedings{Kucklick_2022, title={Visual Interpretability of Image-based
Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference
on System Sciences (HICSS-55)}, author={Kucklick, Jan-Peter}, year={2022} }'
chicago: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate
Appraisal.” In 55th Annual Hawaii International Conference on System Sciences
(HICSS-55), 2022.
ieee: J.-P. Kucklick, “Visual Interpretability of Image-based Real Estate Appraisal,”
presented at the Hawaii International Conference on System Science (HICSS), Virtual,
2022.
mla: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.”
55th Annual Hawaii International Conference on System Sciences (HICSS-55),
2022.
short: 'J.-P. Kucklick, in: 55th Annual Hawaii International Conference on System
Sciences (HICSS-55), 2022.'
conference:
end_date: 2022-01-07
location: Virtual
name: Hawaii International Conference on System Science (HICSS)
start_date: 2022-01-03
date_created: 2021-11-17T07:08:15Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence (XAI)
- Regression Activation Maps
- Real Estate Appraisal
- Convolutional Block Attention Module
- Computer Vision
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/79519/0149.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Visual Interpretability of Image-based Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '34617'
author:
- first_name: Marco
full_name: Huber, Marco
last_name: Huber
- first_name: Philipp
full_name: Terhörst, Philipp
id: '97123'
last_name: Terhörst
- first_name: Florian
full_name: Kirchbuchner, Florian
last_name: Kirchbuchner
- first_name: Naser
full_name: Damer, Naser
last_name: Damer
- first_name: Arjan
full_name: Kuijper, Arjan
last_name: Kuijper
citation:
ama: Huber M, Terhörst P, Kirchbuchner F, Damer N, Kuijper A. Stating Comparison
Score Uncertainty and Verification Decision Confidence Towards Transparent Face
Recognition. 33nd British Machine Vision Conference 2022. Published online
2022. doi:10.48550/ARXIV.2210.10354
apa: Huber, M., Terhörst, P., Kirchbuchner, F., Damer, N., & Kuijper, A. (2022).
Stating Comparison Score Uncertainty and Verification Decision Confidence Towards
Transparent Face Recognition. 33nd British Machine Vision Conference 2022.
https://doi.org/10.48550/ARXIV.2210.10354
bibtex: '@article{Huber_Terhörst_Kirchbuchner_Damer_Kuijper_2022, title={Stating
Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent
Face Recognition}, DOI={10.48550/ARXIV.2210.10354},
journal={33nd British Machine Vision Conference 2022}, publisher={arXiv}, author={Huber,
Marco and Terhörst, Philipp and Kirchbuchner, Florian and Damer, Naser and Kuijper,
Arjan}, year={2022} }'
chicago: Huber, Marco, Philipp Terhörst, Florian Kirchbuchner, Naser Damer, and
Arjan Kuijper. “Stating Comparison Score Uncertainty and Verification Decision
Confidence Towards Transparent Face Recognition.” 33nd British Machine Vision
Conference 2022, 2022. https://doi.org/10.48550/ARXIV.2210.10354.
ieee: 'M. Huber, P. Terhörst, F. Kirchbuchner, N. Damer, and A. Kuijper, “Stating
Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent
Face Recognition,” 33nd British Machine Vision Conference 2022, 2022, doi:
10.48550/ARXIV.2210.10354.'
mla: Huber, Marco, et al. “Stating Comparison Score Uncertainty and Verification
Decision Confidence Towards Transparent Face Recognition.” 33nd British Machine
Vision Conference 2022, arXiv, 2022, doi:10.48550/ARXIV.2210.10354.
short: M. Huber, P. Terhörst, F. Kirchbuchner, N. Damer, A. Kuijper, 33nd British
Machine Vision Conference 2022 (2022).
date_created: 2022-12-20T14:30:02Z
date_updated: 2023-01-23T13:53:14Z
department:
- _id: '761'
doi: 10.48550/ARXIV.2210.10354
keyword:
- Computer Vision and Pattern Recognition (cs.CV)
- 'FOS: Computer and information sciences'
- 'FOS: Computer and information sciences'
language:
- iso: eng
publication: 33nd British Machine Vision Conference 2022
publisher: arXiv
status: public
title: Stating Comparison Score Uncertainty and Verification Decision Confidence Towards
Transparent Face Recognition
type: journal_article
user_id: '97123'
year: '2022'
...
---
_id: '17236'
abstract:
- lang: eng
text: 'The behavior for a humanoid robot is often modeled in accordance with human
behavior. Current research suggests that analyzing infant behavior as a basis
for designing the robot behavior can guide us to a natural robot interface. Based
on this idea many researchers support saliency systems as a bottom-up inspired
way to simulate infant-like gazing behavior. In the field of saliency systems
many different approaches have proposed and quantified in terms of speed, quality
and other technical issues. But so far, no one compared and quantified them in
terms of natural infant tutor interaction. The question we would like to address
in this paper is: Can state-of-the-art saliency systems model infant gazing behavior
in tutoring situations? By addressing these issues we want to take a step towards
an autonomous robot system, which could be used more natural interaction experiments
in future.'
author:
- first_name: Vikram
full_name: Narayan, Vikram
last_name: Narayan
- first_name: Katrin Solveig
full_name: Lohan, Katrin Solveig
last_name: Lohan
- first_name: Marko
full_name: Tscherepanow, Marko
last_name: Tscherepanow
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
citation:
ama: Narayan V, Lohan KS, Tscherepanow M, Rohlfing K, Wrede B. Can state-of-the-art
saliency systems model infant gazing behavior in tutoring situations? Frontiers
in Computational Neuroscience. 2011;5(35). doi:10.3389/conf.fncom.2011.52.00035
apa: Narayan, V., Lohan, K. S., Tscherepanow, M., Rohlfing, K., & Wrede, B.
(2011). Can state-of-the-art saliency systems model infant gazing behavior in
tutoring situations? Frontiers in Computational Neuroscience, 5(35).
https://doi.org/10.3389/conf.fncom.2011.52.00035
bibtex: '@article{Narayan_Lohan_Tscherepanow_Rohlfing_Wrede_2011, title={Can state-of-the-art
saliency systems model infant gazing behavior in tutoring situations?}, volume={5},
DOI={10.3389/conf.fncom.2011.52.00035},
number={35}, journal={Frontiers in Computational Neuroscience}, publisher={Frontiers
Media SA}, author={Narayan, Vikram and Lohan, Katrin Solveig and Tscherepanow,
Marko and Rohlfing, Katharina and Wrede, Britta}, year={2011} }'
chicago: Narayan, Vikram, Katrin Solveig Lohan, Marko Tscherepanow, Katharina Rohlfing,
and Britta Wrede. “Can State-of-the-Art Saliency Systems Model Infant Gazing Behavior
in Tutoring Situations?” Frontiers in Computational Neuroscience 5, no.
35 (2011). https://doi.org/10.3389/conf.fncom.2011.52.00035.
ieee: 'V. Narayan, K. S. Lohan, M. Tscherepanow, K. Rohlfing, and B. Wrede, “Can
state-of-the-art saliency systems model infant gazing behavior in tutoring situations?,”
Frontiers in Computational Neuroscience, vol. 5, no. 35, 2011, doi: 10.3389/conf.fncom.2011.52.00035.'
mla: Narayan, Vikram, et al. “Can State-of-the-Art Saliency Systems Model Infant
Gazing Behavior in Tutoring Situations?” Frontiers in Computational Neuroscience,
vol. 5, no. 35, Frontiers Media SA, 2011, doi:10.3389/conf.fncom.2011.52.00035.
short: V. Narayan, K.S. Lohan, M. Tscherepanow, K. Rohlfing, B. Wrede, Frontiers
in Computational Neuroscience 5 (2011).
date_created: 2020-06-24T13:02:00Z
date_updated: 2023-02-01T12:57:14Z
department:
- _id: '749'
doi: 10.3389/conf.fncom.2011.52.00035
intvolume: ' 5'
issue: '35'
keyword:
- child gazing behavior
- computer vision
- saliency
- development
language:
- iso: eng
publication: Frontiers in Computational Neuroscience
publication_identifier:
issn:
- 1662-5188
publisher: Frontiers Media SA
status: public
title: Can state-of-the-art saliency systems model infant gazing behavior in tutoring
situations?
type: journal_article
user_id: '14931'
volume: 5
year: '2011'
...