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A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking

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E.R. Gast, Michael S. Lew

Computer Vision and Pattern Recognition · Submitted 31 Dec 2010

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This combination together with our switching scheme, makes our tracker very robust against fast movement and motion-blur.

how it is stated · asserted 1.00 read the record →

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arXiv ID1101.0237 (version 1)
Pith IDGS3JRF6D, Pith's permanent record of this paper and its review
CitationE.R. Gast, Michael S. Lew. "A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking." arXiv:1101.0237 (2010). https://pith.science/paper/GS3JRF6D
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,390 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.18 · human participants 0.27 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.06 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This work presents a framework for tracking head movements and capturing the movements of the mouth and both the eyebrows in real-time. We present a head tracker which is a combination of a optical flow and a template based tracker. The estimation of the optical flow head tracker is used as starting point for the template tracker which fine-tunes the head estimation. This approach together with re-updating the optical flow points prevents the head tracker from drifting. This combination together with our switching scheme, makes our tracker very robust against fast movement and motion-blur. We also propose a way to reduce the influence of partial occlusion of the head. In both the optical flow and the template based tracker we identify and exclude occluded points.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This work presents a framework for tracking head movements and capturing the movements of the mouth and both the eyebrows in real-time. contribution 1.00 0.89 0.24 asserted 1.00
s1 We present a head tracker which is a combination of a optical flow and a template based tracker. method 0.67 0.90 0.08 asserted 1.00
s2 The estimation of the optical flow head tracker is used as starting point for the template tracker which fine-tunes the head estimation. method 1.00 0.81 0.07 asserted 1.00
s3 This approach together with re-updating the optical flow points prevents the head tracker from drifting. result 0.41 0.85 0.11 asserted 1.00
s4 This combination together with our switching scheme, makes our tracker very robust against fast movement and motion-blur. result 0.56 0.86 0.24 asserted 1.00
s5 We also propose a way to reduce the influence of partial occlusion of the head. contribution 0.97 0.89 0.05 asserted 0.98
s6 In both the optical flow and the template based tracker we identify and exclude occluded points. method 0.95 0.92 0.04 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.55.

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  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This work presents a framework for tracking head movements and capturing the movements of the mouth and both the eyebrows in real-time. We present a head tracker which is a combination of a optical flow and a template based tracker. The estimation of the optical flow head tracker is used as starting point for the template tracker which fine-tunes the head estimation. This approach together with re-updating the optical flow points prevents the head tracker from drifting. This combination together with our switching scheme, makes our tracker very robust against fast movement and motion-blur. We also propose a way to reduce the influence of partial occlusion of the head. In both the optical flow and the template based tracker we identify and exclude occluded points.

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