Towards Automated Decision Making in Dating Apps Through Pupillary

Responses

Jan Ehlers

1 a

, Sebastian Laverde Alfonso

and Arup Mazumder

Department of Computer Science, Bauhaus-Universit

at Weimar, Schwanseestr. 143, Weimar, Germany

Unstructured.io, 7580 Horseshoe Bar Rd., Loomis, CA, U.S.A.

University of Rhode Island, 45 Upper College Rd, Kingston, RI, U.S.A.

Keywords:

Cognitive Pupillometry, Affective Processing, Arousal, Decision Making, Automation.

Abstract:

Decision making is a multi-stage process that involves a series of rational evaluations. Recently, bodily arousal

has been identiﬁed as a factor that mediates individual decisions, particularly during partner selection. The

current study investigates pupil size changes in response to facial images of the opposite sex from controlled

eye-tracking data (Experiment 1) and by reading out signals from front-facing smartphone cameras in noisy

environments (Experiment 2). The aim is to enable automated decision-making in dating apps using arousal-

based information. The rating results showed a tendency towards moderate evaluations when coping with

facial attractiveness, while pupil diameter did not clearly discriminate between all four rating categories. How-

ever, a ROCKET model was trained on the pupil data from Experiment 1 with a prediction accuracy of 77%

for binary classiﬁcation of clearly preferred and non-preferred images. Ambiguous responses will therefore

continue to pose a problem for cognition-aware systems. Capturing pupil diameter from mobile phone cam-

eras resulted in a high proportion of inadequate recordings, probably due to a lack of experimental control.

However, an overly systematic approach should run contrary to the intended scenario of lifelike mobile dating

app usage.

1 INTRODUCTION

Over the past decades, online dating has become in-

creasingly popular with more than 200 million ac-

tive users by the end of 2019 (Castro and Barrada,

2020). In recent years, however, regular use has

shifted to mobile dating apps that feature real-time

location based services and typically require users to

decide for or against a candidate’s photos by swiping

left (rejection) or right (acceptation) across the screen

(Sawyer et al., 2018; Wu and Trottier, 2022). This

image-based approach is based on the view that phys-

ical appearance exerts a decisive inﬂuence on part-

ner selection (Berscheid and Walster, 1974). Tally-

ing with this, evolutionary psychology suggests that

phenotypic features of the face (more than body char-

acteristics) are particularly well suited to predicting

overall attraction (Fink and Penton-Voak, 2002). And

although the relevance of facial cues appears to differ

between the sexes (Buss, 2016), both men and women

will pay more attention to attractive faces compared to

https://orcid.org/0000-0002-4475-2349

unattractive ones (Dai et al., 2010; Hahn and Perrett,

2014). Thus, today’s dating apps meet our needs dur-

ing partner search with the result that they are used

frequently and in almost any environment. The bi-

nary selection mechanism (reject vs accept) features a

straightforward design, but manual input can become

tiring during long-term usage and even prone to in-

put errors while on the go or in case of one-handed

operation.

Decision making - in dating apps but also in gen-

eral - constitutes a multistage process that involves a

series of (rational) judgments. However, when the

stakes are high and solutions have long-term conse-

quences, individual choices are strongly inﬂuenced

by visceral factors (due to anxiety) or drive states

(e.g. sexual desire) (Damasio, 1996). Especially

sexual arousal has recently gained recognition as a

situational factor mediating individual mate choice

(Skakoon-Sparling et al., 2016). The current work

explores arousal-based changes in response to facial

images of the opposite sex. To quantify arousal we

aim for a peripheral measure that can be administered

remotely and is applicable in different settings. As

522

Ehlers, J., Alfonso, S. and Mazumder, A.

Towards Automated Decision Making in Dating Apps Through Pupillary Responses.

DOI: 10.5220/0012471100003660

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 1: GRAPP, HUCAPP

and IVAPP, pages 522-529

ISBN: 978-989-758-679-8; ISSN: 2184-4321

indicated above, visual cognition is ubiquitous during

partner selection, suggesting eye-based data to con-

stitute a promising channel for incorporating bodily

activation.

Pupil diameter primarily reﬂects the nervous sys-

tem response to ambient luminance (’pupillary light

reﬂex’) (Kardon, 1995). However, in controlled light-

ing environments, pupil size changes are supposed to

reﬂect processes underlying cognitive functions such

as attention (Einh

auser et al., 2008) or working mem-

ory (Kucewicz et al., 2018; Grimmer et al., 2021).

Affective pupillometry relies on innervations from the

autonomic nervous system and indicates that pupil di-

ameter increases with general bodily arousal (Ehlers

et al., 2016; Ehlers et al., 2018). Liao et al. (Liao

et al., 2021) as well as Pronk et al. (Pronk et al., 2021)

report an increase in pupil diameter when viewing at-

tractively rated faces. Laeng and Falkenberg (Laeng

and Falkenberg, 2007) observe strong dilations in fe-

male participants to pictures of their sexual partners.

Tallying with this, Rieger and Savin-Williams (Rieger

and Savin-Williams, 2012) identify pupil diameter as

an indicator of sexual orientation, with heterosexual

men showing greater dilations to images of women

than vice versa. Bernick et al. (Bernick et al., 1971)

report pupil diameter to increase with sexual arousal

and conclude that pupil size changes can be applied

to distinguish between sexual arousal and more gen-

eral states of activation. However, studies on affective

pupillometry tend to apply cumulative results over a

period of time after stimulus-onset and to ignore time-

dependent changes in pupil diameter. And although

research has focused almost exclusively on laboratory

experiments, results are often confounded with stim-

ulus luminance or contrast (Liao et al., 2021).

The present study builds on this research. The

overall aim is to enable arousal-based decision mak-

ing via pupil responses in order to automate the use

of dating apps. Therefore, we investigate pupil size

changes in response to facial images of the oppo-

site sex in a fully controlled laboratory experiment

(Experiment 1). A state-of-the-art machine learning

model is applied to predict individual choices by clas-

sifying pupil responses. Experiment 2 is a ﬁeld study

that adopts the previous design but reads out pupil di-

ameter from front-facing smartphone cameras. The

results will show the extent to which the laboratory

ﬁndings can be transferred to real-life conditions.

2 EXPERIMENT 1

Experiment 1 investigates changes in pupil diameter

when viewing facial stimuli of the opposite sex. En-

vironmental factors are kept constant in order to de-

termine inﬂuences from cognitive and affective pro-

cessing while coping with facial attractiveness.

2.1 Methods

The following subsections provide information on the

experimental design and specify details on data pro-

cessing and sample characteristics.

2.1.1 Design and Procedure

Experiment 1 was carried out in a laboratory room

featuring a constant illumination of 300 lux. Partici-

pants received an introduction to the research subject

and were then seated in an upright position in front

of a computer monitor. A stationary Eye-tracker was

attached to the lower edge of the screen at a distance

of 90cm.

We applied a within-subject design by providing

a set of 60 high-quality AI-generated images that was

randomly taken from a pool of 400 stimuli (Fig. 1A)

(Karras et al., 2019). Each image showed a human

face that looked directly into the camera with no other

object in the foreground or background. Our volun-

teers identiﬁed as heterosexual and were asked to pas-

sively view 30 images of the opposite sex. Each im-

age was presented for ﬁve seconds against a light grey

background and preceded by a one-second baseline

recording. Baseline acquisition involved the presen-

tation of a pixel scrambled version of the subsequent

image to control for differences in luminance and con-

trast (Fig.1B).

Figure 1: A: AI-generated images. B: Trial procedure dur-

ing the image viewing task.

Subsequent to each image presentation, volunteers

were asked to evaluate the corresponding face on a

Towards Automated Decision Making in Dating Apps Through Pupillary Responses

523

four-point scale ranging from (1) “Do not like at all”,

(2) “Rather do not like” to (3) “Rather like” and (4)

“Totally like”. After the individual assessment par-

ticipants proceeded to the next trial via button press.

Pupil diameter was recorded throughout the entire ex-

periment (approx. ﬁve minutes).

2.1.2 Data Processing

Eye-tracking data was obtained binocular with a sam-

pling rate of 60 Hz. According to (Mantiuk et al.,

2012), data from the left eye were taken to deter-

mine changes in pupil diameter. Following (Leys

et al., 2013), we applied the median absolute devia-

tion (MAD) to correct for outliers. Recordings mainly

included blink artefacts, i.e. segments of missing data

as a result of eyelid closure. Exemplary raw data tri-

als are depicted in Figure 2. Outliers were removed

from the data set and replaced via linear interpolation.

For purposes of presentation, eye-tracking data were

smoothed using a moving window of 25 data points.

Figure 2: Exemplary raw data trials featuring pupil size

changes during the image viewing task. Vertical lines in-

dicate blink artefacts (segments of missing data).

2.1.3 Apparatus

Pupil size changes were measured with the Tobii Pro

Nano Eye-tracker, Tobii Pro Lab Software (v. 1.123)

was applied for data recording and storage. The Eye-

tracker was attached to a 22-inch Dell P2213 monitor

with a resolution of 1680 x 1050 pixels. Statistical

analyses were carried out using the open-source soft-

ware JASP (v.0.12.2) (Love et al., 2019).

2.1.4 Particiants

16 volunteers (ﬁve female, M: 28 years (SD: 3)), all

of them students of computer science at Bauhaus-

Universit

at, participated in Experiment 1. Informa-

tion on regular medication was not collected, how-

ever, volunteers reported no history of head injury and

no neurological or psychiatric disorders. Written in-

formed consent was obtained prior to the experiment.

All measurements were performed in accordance with

the Declaration of Helsinki and approved by a local

ethics committee (Ulm University).

2.2 Results

The following subsections summarize our ﬁndings on

rating decisions, pupil size changes and data mod-

elling.

2.2.1 Ratings

Participants were asked to evaluate 30 facial images

of the opposite sex on basis of a four-point scale.

16% of all cases were rated as (1) “Do not like at all”

whereas the majority of faces were either assessed as

(2) “Rather do not like” (35%) or (3) “Rather like”

(34%). 14 % were classiﬁed as (4) “Totally like”.

Four participants allocated images to categories 2 and

3 only.

2.2.2 Pupillometry

Absolute pupil diameter differs between participants.

To make individual responses comparable, we aver-

aged across the last 30 data points of baseline record-

ing and subtracted the result from each value during

the ﬁve seconds of image viewing (Grimmer et al.,

2021). P-values of the Shapiro-Wilk test indicated

that pupillary data was well modelled by a normal dis-

tribution.

The ﬁrst half second after stimulus-onset involved

a pupil constriction that appeared comparably strong

while viewing disliked facial images (category 1).

Hereafter, pupil diameter increased and diverged ac-

cording to feelings of affection after approx. two sec-

onds. Averaged responses to preferred images (cate-

gory 3 and 4) continued to increase whereas pupil di-

ameter yielded a temporary constriction while view-

ing (rather) disliked images (category 1 and 2) (Fig.

3, left). Figure 4 depicts exemplary responses (indi-

vidual averages) to images that were rated as ”Do not

like at all” (category 1, left) and ”Totally like” (cate-

gory 4, right). As can be seen, variability between in-

dividuals increased with increasing viewing duration,

featuring similar courses only during the ﬁrst second

after stimulus-onset.

Descriptive statistics suggested a tendency to-

wards larger diameters in response to attractively

rated faces (Fig. 3, right). Facial images of cat-

egory 1 (“Do not like at all”) involved an increase

of 0.09mm (SE: 0.03) compared to baseline mean

whereas category 2 stimuli (“Rather do not like”) in-

duced diameter changes of 0.11mm (SE: 0.02). Av-

eraged values during image viewing from category 3

(“Rather like”) proved to be similar (M: 0.11mm, SE:

0.02) whereas facial images from category 4 (“To-

tally like”) were associated with the strongest increase

in pupil diameter (M: 0.13mm, SE: 0.02). However,

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Figure 3: Total averages (n=16) of pupil diameter while viewing images of the opposite sex. Values depict differences from

baseline mean and are grouped according to individual preferences. Left: Zero on the x-axis indicates stimulus-onset. For

purposes of presentation, signal course changes are smoothed using a moving window of 25 data points.

Figure 4: Individual averages (n=11) of pupil diameter while viewing images of the opposite sex that were rated as ”Do not

like at all” (category 1, left) and ”Totally like” (category 4, right). Values depict differences from baseline mean; zero on the

x-axis indicates stimulus-onset. For purposes of presentation, signal course changes are smoothed using a moving window of

25 data points.

a one-way repeated measures ANOVA (Greenhouse-

Geisser corrected) indicated no considerable differ-

ences (F(2.541, 27.950)= 1.149, p= 0.34).

In a further step, we compared pupillary responses

that involved clear preferences (category 1 vs cate-

gory 4) via a paired samples t-test and observed a

strong trend towards larger dilations while viewing

attractively rated faces (t(11)= -1.536, p= .08, d= -

0.44). However, this excludes trials that entail some

degree of uncertainty (category 2 and 3). Follow-

ing the selection technique from mobile dating apps,

we regrouped pupillary data into binary samples by

merging category 1 and 2 as well as category 3 and

4 and computed weighted averages due to different

number of data sets. In a subsequent t-test, the effect

of category was no longer visible (t(11)= -0.184, p=

.43). Four participants were excluded from inferential

statistics as they allocated images only to categories 2

and 3.

2.2.3 Time Series Classifciation

Machine learning (ML) methods enable to identify

complex correlations in time series data by ﬁgur-

ing out patterns and extracting useful features (Ruiz

et al., 2021). We implemented an ML-model that was

trained on the pupillary data from Experiment 1. To

reduce ambiguity, we addressed data from only those

trials that indicated clear preferences (category 1 and

4). Along with the outlier correction this resulted in

a total of 79% data reduction. However, data quality

was considerably increased and should be particularly

suited for the model to learn from it. The remaining

data were standardized by re-scaling the distribution

(M:0, SD:1), series lengths were normalized to adjust

the number of decimals.

We applied the state-of-the-art ROCKET model

(Random Convolutional Kernel Transform) that fea-

tures a high classiﬁcation accuracy by transforming

Towards Automated Decision Making in Dating Apps Through Pupillary Responses

525

time series on basis of random convolutional kernels

and training a linear classiﬁer (Dempster et al., 2020).

Data was split using Stratisﬁed Kfold (k=5) to ensure

a balanced proportion of both labels resulting in 102

samples to train ROCKET and 26 samples to test it.

Given the small data sets we selected the evaluation

strategy LOO (LeaveOneOut) built in a Ridge Classi-

ﬁer. Training was carried out ﬁve times using differ-

ent random seeds on top of Stratisﬁed Kfold to cap-

ture the best model (25 models trained in total). Table

1 indicates relevant metrics for the best obtained uni-

variate ROCKET model featuring a prediction accu-

racy of at least 77% during binary classiﬁcation (cat-

egory 1 vs 4).

2.3 Discussion

Volunteers classiﬁed 30 facial images into four cate-

gories according to their individual preferences. Re-

sults suggest a tendency towards moderate ratings

when dealing with facial attractiveness. Dislikes (cat-

egory 1) as well as strong likes (category 4) account

for only a small proportion of cases (around 30%),

suggesting that ambiguous responses and a degree of

uncertainty pose an ongoing challenge for cognition-

aware systems. It could be argued that our ﬁve female

volunteers saw different faces due to the presentation

of images of the opposite-sex. This is a valid objec-

tion and further research may balance the sex ratio

or focus on one sex only. On the other hand, recent

studies have found no differences in the pupillary re-

sponses of heterosexual men and women to images of

the opposite sex (Attard-Johnson et al., 2021).

Due to baseline referencing and controlled envi-

ronmental conditions, current changes in pupil size

cannot be explained through low-level factors such

as luminance or contrast. During early processing

stages (up to 1 second) pupil diameter was charac-

terized by low variability between individuals and

likely shaped by cognitive processes that underlying

attention (Math

ot et al., 2014) or memory processes

(Naber et al., 2013) during face recognition. Two sec-

onds after stimulus onset, pupil responses diverged

according to attractiveness ratings. While pupil re-

actions to unfavourable images (category 1 and 2) de-

creased, there was a strong and sustained autonomous

response to favourable images (category 3 and 4).

These ﬁndings replicate previous studies, indicating

pupil diameter to increase when viewing preferred fa-

cial images of the opposite sex (Bernick et al., 1971;

Laeng and Falkenberg, 2007; Liao et al., 2021; Rieger

and Savin-Williams, 2012). However, arousal-related

changes in pupil size did not clearly distinguish be-

tween all four categories. Rather, it appeared that

pupil diameter was only sensitive to discriminating

physical states of clear rejection from those of clear

acceptance. More recently, Pronk et al. (Pronk et al.,

2021) conducted a similar study with a larger num-

ber of participants and conﬁrmed a positive asso-

ciation between pupil diameter and the acceptance

of a hypothetical partner. Larger samples allow for

more precise estimation of statistical properties and

greater power; therefore it is reasonable to assume

that a larger sample would yield similar results in the

present case.

The ROCKET model yielded a promising perfor-

mance. Although it’s slightly biased towards the most

positive category (4), the model predicted results for

77% of unseen data correctly. For comparison only:

A dummy classiﬁer randomly classifying samples or

constantly predicting the same class provided an ac-

curacy of no better than 57%.

3 EXPERIMENT 2

Experiment 2 adapts the design from Experiment 1.

However, the setting is transferred from fully con-

trolled laboratory conditions to various domestic loca-

tions. Moreover, video-based eye-tracking is replaced

by recordings from unmodiﬁed smartphone cameras.

3.1 Methods

The following subsections provide information on the

experimental design and specify details on technical

implementation, data processing and sample charac-

teristics.

Table 1: Classiﬁcation metrics of the univariate ROCKET model for pupillary responses to facial images of category 1 (”Do

not like at all”) and category 4 (”Totally like”).

Precision Recall F1-score Number of samples

1 (category 4) 71% 92% 80% 13

0 (category 1) 89% 92% 73% 13

Accuracy 77% 26

Macro avg 80% 92% 76% 26

Weighted avg 80% 92% 76% 26

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3.1.1 Design and Procedure

Subsequent to Experiment 1 we collected another

sample of volunteers in order to pilot a ﬁeld study in

domestic environments. A new set of images (30 male

and 30 female faces) was selected from the same pool

of 400 AI-generated pictures (Karras et al., 2019). An

Android package (APK) including all relevant ﬁles

was emailed to the participants. They were asked to

install it on their smartphones and grant access to the

mobile storage media in order to acquire video data

from the front-facing camera. Experimental design

and task procedure corresponded to the setting de-

picted in 2.1.1. Volunteers were asked to pick a quiet

moment and sit comfortably while holding the phone

steady in front of their face. However, task process-

ing was not supervised and inﬂuencing factors like

viewing angle, distance from screen or ambient light-

ing could not be controlled. Upon completion of the

viewing task, all video ﬁles were uploaded to a server.

3.1.2 Technical Implementation

Pupillary data was captured from video ﬁles of the

front-facing camera of mobile phones. To obtain

the center of the pupil, we applied the deep learn-

ing framework GazeML which constitutes an estab-

lished method to recognize landmarks of the eyes

(Park et al., 2018). RGB values were employed for

pupil detection and comparison with the closest re-

gion’s color codes. To detect a similar colored pixel,

we contrasted nearby regions until color values ex-

ceeded a predetermined range of black shading (RGB

values 51,51,51).

3.1.3 Data Processing

Mobile devices featured varying frame rates and we

normalized sampling to a constant value of 25 frames

per second (fps). As during Experiment 1, the MAD

algorithm was applied to detect outliers (Leys et al.,

2013). However, while reading out pupillary data, rel-

evant features could not entirely be captured frame to

frame from video ﬁles which resulted in fragmentary

data sets (Fig. 5). As a consequence, seven partic-

ipants had to be removed from the data base due to

insufﬁcient recordings.

3.1.4 Apparaus

Pupil diameter was determined on basis of video ﬁles

from the front-facing camera of latest Samsung smart-

phones. Android Studio, the development environ-

ment of Google’s operating system, was used to de-

sign the front end of the mobile app. The Python ﬂask

Figure 5: Exemplary raw data trials featuring pupil size

changes during the image viewing task. Data were captured

from video ﬁles of front-facing smartphone cameras. Verti-

cal lines indicate segments of missing data).

framework was applied to create the backends’ REST

API.

3.1.5 Participants

12 male volunteers (M: 28 years, SD: 2) partici-

pated in Experiment 2. Seven data sets had to be

excluded from pupillometric analyses due to unsuc-

cessful recordings. Information on regular medica-

tion was not collected, however, volunteers reported

no history of head injury and no neurological or psy-

chiatric disorder. Written informed consent was ob-

tained prior to the experiment. All measurements

were performed in accordance with the Declaration

of Helsinki.

3.2 Results

The following subsections summarize ﬁndings on rat-

ing decisions and pupil size changes.

3.2.1 Ratings

Participants were asked to evaluate 30 facial images

of the opposite sex on basis of a four-point scale. 14%

of all cases were rated as “Do not like at all” whereas

the majority of faces were either assessed as “Rather

do not like” (40%) or “Rather like” (29%). 17% of

cases were classiﬁed as “Totally like”.

3.2.2 Pupillometry

A large part of pupillary data from the video ﬁles was

not analysable. Due to the small number of successful

recordings (n=5), we forego the documentation of de-

scriptive statistics and focus a qualitative evaluation.

During the ﬁrst second of task processing, averaged

pupil size changes roughly corresponded to the eye-

tracking data during Experiment 1, indicating pupil

diameter to increase while viewing attractively rated

faces (Fig. 6). Smartphone recordings then yielded

strong responses to facial images from category 3

(approx. 0.3mm above baseline mean), whereas di-

ameters fell below baseline level while viewing pic-

Towards Automated Decision Making in Dating Apps Through Pupillary Responses

527

tures from category 2. Pupillary reactions to non-

preferred images (category 1) indicated a clear dila-

tion whereas pictures from category 4 (”Totally like”)

induced rather unspeciﬁc dynamics.

Figure 6: Total averages (n=5) of pupil diameter while

viewing images of the opposite sex. Values depict differ-

ences from baseline mean and are grouped according to in-

dividual preferences; zero on the x-axis indicates stimulus-

onset. Data were captured from video ﬁles of the mobile

phone front-facing camera.

3.3 Discussion

We applied the viewing task from Experiment 1 in a

noisy setting and during unsupervised task process-

ing, while pupil data were captured by video ﬁles

from front-facing smartphone cameras. Rating results

from Experiment 1 were essentially replicated, indi-

cating a tendency towards moderate ratings during at-

tractiveness judgments. However, there was a large

proportion of insufﬁent data for pupillometry. In addi-

tion, successful recordings (n=5) yielded inconsistent

results, featuring both a sustained drop below baseline

level (category 2) and a particularly strong response

(category 3) when processing rather moderate stim-

uli.

Raﬁqi et al. (Raﬁqi et al., 2015) obtained pupil

size changes from unmodiﬁed smartphone cameras

in a fully controlled setting and reported high corre-

lations with control measures from a certiﬁed Eye-

tracker. Therefore, it can be assumed that our data

loss was mainly due to processing issues, including

low ambient lighting and unfavourable viewing dis-

tances or camera angles. In addition, the current

sample consisted of volunteers from an international

study program, including a large proportion of Indian

students with rather dark eyes. This presents an addi-

tional challenge to the pupil detection algorithm, es-

pecially under unfavourable lighting conditions. The

latter may be countered by environmental illumina-

tion compensation as recently introduced by Gollan

et al. (Gollan and Ferscha, 2016). However, cogni-

tive pupillometry based on digital cameras requires a

certain degree of environmental control and a rather

stationary camera position.

4 CONCLUSION

The present study takes a further step towards auto-

mated decision making in dating apps using arousal-

based information. We applied pupillometry as an

established method to map transient and long-lasting

changes in underlying cognitive and affective pro-

cessing. Under laboratory conditions, pupil diam-

eter discriminated between states of clear rejection

and clear acceptance. Accordingly, data modelling

yielded promising results to enable proper automa-

tion of binary classiﬁcations. However, a controlled

setting runs contrary to the intended scenario of life-

like mobile dating app usage. Furthermore, the ques-

tion of whether this type of automation is desirable

in principle remains unanswered. A perceived loss of

control during partner selection could lead to rejec-

tion of the application. Therefore, usability measures

and qualitative data analyses should accompany fur-

ther research to ensure a positive user experience.

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