Oculum afficit: Ocular Affect Recognition

Elmar Langholz

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Oculum afficit: Ocular Affect Recognition

Elmar Langholz

Computer Vision and Pattern Recognition · Submitted 22 May 2019

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

The authors' main claim Due to this, we propose a methodology in which we can accurately infer the overall affect of a person by looking at the ocular region of an individual.

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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-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,196 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.12 · human participants 0.40 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.08 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

Recognizing human affect and emotions is a problem that has a wide range of applications within both academia and industry. Affect and emotion recognition within computer vision primarily relies on images of faces. With the prevalence of portable devices (e.g. smart phones and/or smart glasses),acquiring user facial images requires focus, time, and precision. While existing systems work great for full frontal faces, they tend to not work so well with partial faces like those of the operator of the device when under use. Due to this, we propose a methodology in which we can accurately infer the overall affect of a person by looking at the ocular region of an individual.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Recognizing human affect and emotions is a problem that has a wide range of applications within both academia and industry.	background 1.00	0.04	0.03	asserted 1.00
s1	Affect and emotion recognition within computer vision primarily relies on images of faces.	background 1.00	0.05	0.03	asserted 1.00
s2	With the prevalence of portable devices (e.g. smart phones and/or smart glasses),acquiring user facial images requires focus, time, and precision.	background 0.86	0.08	0.02	asserted 1.00
s3	While existing systems work great for full frontal faces, they tend to not work so well with partial faces like those of the operator of the device when under use.	gap 0.71	0.10	0.04	asserted 0.93
s4	Due to this, we propose a methodology in which we can accurately infer the overall affect of a person by looking at the ocular region of an individual.	contribution 0.99	0.63	0.68	asserted 0.69

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.92.

What was examined, and what was not

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arXiv listing abstract v1

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full text
figures and tables
references and citing sentences
relations to other papers

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P abstract

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S sections
T body sentences
C citations
R cross-paper relations

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

Recognizing human affect and emotions is a problem that has a wide range of applications within both academia and industry. Affect and emotion recognition within computer vision primarily relies on images of faces. With the prevalence of portable devices (e.g. smart phones and/or smart glasses),acquiring user facial images requires focus, time, and precision. While existing systems work great for full frontal faces, they tend to not work so well with partial faces like those of the operator of the device when under use. Due to this, we propose a methodology in which we can accurately infer the overall affect of a person by looking at the ocular region of an individual.

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1 arXiv paper cite this in 2 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 1 sentence without a confident label

Cites it as background 1 paper

In [ref] a CNN-based method is proposed for predicting valence and arousal in images by focusing on the ocular region.

BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient · Dimensional Model of Affect · 0.93

What it cites

Cites as background 1 paper

Furthermore, generation of facial expression from a neutral expression using Identity-free conditional Generative Adversarial Network (IF-GAN) [ref] demonstrates another use following along this area of research.

Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network · Related work · 0.97

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