Combination of multiple measurement cues for visual face tracking

Nikolaos Katsarakis, Aristodemos Pnevmatikakis, Zheng-Hua Tan, Ramjee Prasad

Research output: Contribution to journalJournal articleResearchpeer-review

4 Citations (Scopus)

Abstract

Visual face tracking is an important building block for all intelligent living and working spaces, as it is able to locate persons without any human intervention or the need for the users to carry sensors on themselves. In this paper we present a novel face tracking system built on a particle filtering framework that facilitates the use of non-linear visual measurements on the facial area. We concentrate on three different such non-linear visual measurement cues, namely object detection, foreground segmentation and colour matching. We derive robust measurement likelihoods under a unified representation scheme and fuse them into our face tracking algorithm. This algorithm is complemented with optimum selection of the particle filter’s object model and a target handling scheme. The resulting face tracking system is extensively evaluated and compared to baseline ones.
Original languageEnglish
JournalWireless Personal Communications
Volume78
Issue number3
Pages (from-to)1789-1810
ISSN0929-6212
DOIs
Publication statusPublished - 2014

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Katsarakis, Nikolaos ; Pnevmatikakis, Aristodemos ; Tan, Zheng-Hua ; Prasad, Ramjee. / Combination of multiple measurement cues for visual face tracking. In: Wireless Personal Communications. 2014 ; Vol. 78, No. 3. pp. 1789-1810.
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Combination of multiple measurement cues for visual face tracking. / Katsarakis, Nikolaos; Pnevmatikakis, Aristodemos; Tan, Zheng-Hua; Prasad, Ramjee.

In: Wireless Personal Communications, Vol. 78, No. 3, 2014, p. 1789-1810.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Katsarakis, Nikolaos

AU - Pnevmatikakis, Aristodemos

AU - Tan, Zheng-Hua

AU - Prasad, Ramjee

PY - 2014

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AB - Visual face tracking is an important building block for all intelligent living and working spaces, as it is able to locate persons without any human intervention or the need for the users to carry sensors on themselves. In this paper we present a novel face tracking system built on a particle filtering framework that facilitates the use of non-linear visual measurements on the facial area. We concentrate on three different such non-linear visual measurement cues, namely object detection, foreground segmentation and colour matching. We derive robust measurement likelihoods under a unified representation scheme and fuse them into our face tracking algorithm. This algorithm is complemented with optimum selection of the particle filter’s object model and a target handling scheme. The resulting face tracking system is extensively evaluated and compared to baseline ones.

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