Modeling and detection of wrinkles in aging human faces using marked point processes

Nazre Batool; Rama Chellappa

doi:10.1007/978-3-642-33868-7_18

Modeling and detection of wrinkles in aging human faces using marked point processes

Nazre Batool, Rama Chellappa

University of Maryland

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

27 Citations (Scopus)

Abstract

In this paper we propose a new generative model for wrinkles on aging human faces using Marked Point Processes (MPP). Wrinkles are considered as stochastic spatial arrangements of sequences of line segments, and detected in an image by proper localization of line segments. The intensity gradients are used to detect more probable locations and a prior probability model is used to constrain properties of line segments. Wrinkles are localized by sampling MPP using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We also present an evaluation setup to measure the performance of the proposed model. We present results on a variety of images obtained from the Internet to illustrate the performance of the proposed model.

Original language	English
Title of host publication	Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
Publisher	Springer-Verlag
Pages	178-188
Number of pages	11
Edition	PART 2
ISBN (Print)	9783642338670
DOIs	https://doi.org/10.1007/978-3-642-33868-7_18
Publication status	Published - 2012
Externally published	Yes
Event	Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings - Florence, Italy Duration: 7 Oct 2012 → 13 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7584 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
Country/Territory	Italy
City	Florence
Period	7/10/12 → 13/10/12

Keywords

Markov Point Process
Modeling of wrinkles
Reversible Jump Markov Chain Monte Carlo
stochastic geometrical model

Access to Document

10.1007/978-3-642-33868-7_18

Cite this

Batool, N., & Chellappa, R. (2012). Modeling and detection of wrinkles in aging human faces using marked point processes. In Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings (PART 2 ed., pp. 178-188). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7584 LNCS, No. PART 2). Springer-Verlag. https://doi.org/10.1007/978-3-642-33868-7_18

Batool, Nazre ; Chellappa, Rama. / Modeling and detection of wrinkles in aging human faces using marked point processes. Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings. PART 2. ed. Springer-Verlag, 2012. pp. 178-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Modeling and detection of wrinkles in aging human faces using marked point processes",

abstract = "In this paper we propose a new generative model for wrinkles on aging human faces using Marked Point Processes (MPP). Wrinkles are considered as stochastic spatial arrangements of sequences of line segments, and detected in an image by proper localization of line segments. The intensity gradients are used to detect more probable locations and a prior probability model is used to constrain properties of line segments. Wrinkles are localized by sampling MPP using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We also present an evaluation setup to measure the performance of the proposed model. We present results on a variety of images obtained from the Internet to illustrate the performance of the proposed model.",

keywords = "Markov Point Process, Modeling of wrinkles, Reversible Jump Markov Chain Monte Carlo, stochastic geometrical model",

author = "Nazre Batool and Rama Chellappa",

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language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer-Verlag",

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pages = "178--188",

booktitle = "Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings",

edition = "PART 2",

note = "Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings ; Conference date: 07-10-2012 Through 13-10-2012",

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Batool, N & Chellappa, R 2012, Modeling and detection of wrinkles in aging human faces using marked point processes. in Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7584 LNCS, Springer-Verlag, pp. 178-188, Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings, Florence, Italy, 7/10/12. https://doi.org/10.1007/978-3-642-33868-7_18

Modeling and detection of wrinkles in aging human faces using marked point processes. / Batool, Nazre; Chellappa, Rama.
Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings. PART 2. ed. Springer-Verlag, 2012. p. 178-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7584 LNCS, No. PART 2).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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T1 - Modeling and detection of wrinkles in aging human faces using marked point processes

AU - Batool, Nazre

AU - Chellappa, Rama

PY - 2012

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N2 - In this paper we propose a new generative model for wrinkles on aging human faces using Marked Point Processes (MPP). Wrinkles are considered as stochastic spatial arrangements of sequences of line segments, and detected in an image by proper localization of line segments. The intensity gradients are used to detect more probable locations and a prior probability model is used to constrain properties of line segments. Wrinkles are localized by sampling MPP using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We also present an evaluation setup to measure the performance of the proposed model. We present results on a variety of images obtained from the Internet to illustrate the performance of the proposed model.

AB - In this paper we propose a new generative model for wrinkles on aging human faces using Marked Point Processes (MPP). Wrinkles are considered as stochastic spatial arrangements of sequences of line segments, and detected in an image by proper localization of line segments. The intensity gradients are used to detect more probable locations and a prior probability model is used to constrain properties of line segments. Wrinkles are localized by sampling MPP using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We also present an evaluation setup to measure the performance of the proposed model. We present results on a variety of images obtained from the Internet to illustrate the performance of the proposed model.

KW - Markov Point Process

KW - Modeling of wrinkles

KW - Reversible Jump Markov Chain Monte Carlo

KW - stochastic geometrical model

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DO - 10.1007/978-3-642-33868-7_18

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SN - 9783642338670

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 188

BT - Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings

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T2 - Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings

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ER -

Batool N, Chellappa R. Modeling and detection of wrinkles in aging human faces using marked point processes. In Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings. PART 2 ed. Springer-Verlag. 2012. p. 178-188. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-33868-7_18

Modeling and detection of wrinkles in aging human faces using marked point processes

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Keywords

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