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Mason Krohn, Stanford ’24 

 
 
 
Abstract: Facial recognition has experienced an ethical reckoning in academia and popular 
media for its inaccuracy and embedded bias, particularly with regard to its treatment of gender 
minorities. Failures in sampling and overlooked flaws have made the in-use software unusable 
for many queer people. Yet, similar discourse is lacking for gait recognition––automated 
identification based on an individual’s movement. With gait recognition software already 
capable of performing gender classification and already deployed in some sites, how might 
members of the LGBTQ community, who use motion as a social indicator, be affected by the 
application of such technology? In tandem, how does mechanized gait recognition rely on pre-
existing assumptions about queer people? This paper seeks to build on ethical analysis of facial 
recognition software by looking at the harms of gait recognition in pre-computerized contexts, 
inspecting the assumptions used in gait tech development, and evaluating current applications of 
the technology. 
 
 
 
 
 
 
 
 
 
 



 

In January 2020, a letter to the U.S. Privacy and Civil Liberties Board signed by the 
National Center for Transgender Equality and the National LGBTQ Task Force called for an 
immediate suspension of facial recognition systems across the country (“Letter,” 2020). 
Concerns of bias and discrimination cited in the letter echo the actions of municipal lawmakers 
in cities like Oakland and San Francisco who have outright banned facial recognition (Fussell, 
2021). Nevertheless, in 2021 the biometric scan software industry, comprising fingerprint readers 
and facial recognition, grew to $10.2 billion in revenue in the U.S. alone (Curran, 2021). The 
unremitting growth of surveillance companies coupled with alarm over this surveillance from 
queer rights organizations places facial recognition technology at the center of conversations 
about Big Tech’s treatment of LGBTQ people. Yet, biometric surveillance technology in use 
today does not stop short at the chin. Amid revolutions in machine learning, the way we move–– 
our walking posture, our sway, and the weight of our steps–– has also been distilled into 
quantifiable metrics. Known as gait recognition technology, modern software is capable of 
detecting a unique signature from the way an individual walks either through video recordings or 
motion sensors. Although facial recognition technology has undergone a plethora of discourse 
relating to the ways it disenfranchises queer people, gait recognition has not garnered the same 
attention. In response to this gap in scholarship, I contend that the use of gait recognition 
technology poses a threat to queer people. 

I will argue this in three steps. First, by looking at psychological research and cultural 
anecdotes, I will demonstrate that independent from technology, gait functions as a social 
indicator of sexual orientation and gender identity. Second, drawing upon papers from computer 
vision researchers, I will argue that the design of gait recognition software adheres to a strictly 
binary classification system and overlooks the manipulability of gendered cues. Third, by 
investigating the blunders of facial recognition software and the sites where biometric 
technology interacts with queer citizens, I will establish that gait recognition technologies 
replicate the dangers of facial recognition software, harming queer people through exposure and 
misclassification. 
 
Gait Recognition 
 In order to understand the linkages between queer people and gait recognition 
technology, one must first understand the scope of the technology: what it is, how it’s done, and 
where it’s used. Biometric security researchers Mason et al. (2016) define gait biometric 
technology as “extracting and measuring unique and distinctive patterns from human 
locomotion” typically through video cameras, smartphones, and floor sensors (p. vii). As Mason 
et al. (2016) point out, there is no “single measure that encompasses the full set of complex 
dynamics reflecting what we consider to be the human gait,” so instead, machine learning 
algorithms extract certain features by which an individual’s gait can be verified or identified (p. 
9). Essentially, gait recognition technology offers a way to decipher who an individual is based 
on specific components of their style of movement. 



 

 The Chinese company Watrix made headlines when police in Shanghai and Beijing 
began using their gait recognition devices to enhance surveillance systems. Watrix, which has 
raised $14.5 million in funding, is one of the first ventures to commercialize gait recognition 
(Kang, 2018). FST Biometrics, an Israeli gait recognition firm, shut down over technical 
difficulties and corporate infighting, and most other gait recognition development has been 
government-directed or purely academic (Kang, 2018). Watrix’s software maintains a 94 percent 
accuracy rate in identification, acting as a backup to facial recognition which requires close 
range (Kang, 2018). The company advertises its commercially-available device Shui Di Shen 
Jian (水滴神鉴) as being capable of finding a suspect in CCTV footage after being fed a clip of 
the target walking (“SHUI,” n.d.). 
 Gait recognition has certainly not proliferated in the same capacity that facial recognition 
has, but the overwhelming outlook on the innovation is that it can complement pre-existing facial 
recognition systems. Computer vision researchers Do et al. (2020) claim that gait technology is 
preferable to other recognition software because gait is capturable from a distance, difficult to 
forge, and unreliant on the cooperation of the target. Many of these attributes stand out because 
they are not possible with facial recognition software, which for the most part, needs up-close 
shots or high-quality resolutions. Considering that facial recognition is already used by the FBI 
on DMV photos, Customs and Border Protection in airports, and city police departments like that 
of Baltimore, it is likely that the proliferation of gait recognition is not so far-off (Ghaffary and 
Molla, 2019). It is therefore imperative to examine gait recognition technology’s propensity to 
harm queer citizens, given its increasingly relevant role in surveillance. 
 
Before the Machine: Gait as a Social Indicator 

Before delving into mechanized understandings of queerness through gait recognition, 
one must consider the relationship between gait and queerness in entirely social settings. 
Separate from recent innovations in gait biometrics, gait functions as a social indicator of 
sexuality and gender. Professor of Communication and Psychology Kerri Johnson investigates 
how humans measure gender and sexual orientation through gait. In summarizing the work of 
prior social psychologists, she advances that gender is socially inferrable based on features like 
“swaying hips” and “swaggering shoulders” (Johnson, 2007, p. 323). Johnson (2007) furthers 
this argument by hypothesizing that “gender typicality” with regard to “body shape and body 
motion” contribute to determinations of sexual orientation by perceivers (p. 331). Looking at 
both artificial models and real human walking patterns, subjects of her study confirmed this 
hypothesis; they deciphered sexual orientation by referring to how much a person’s (or a 3D 
model’s) gait adheres to gendered expectations of their movement. In “Deliberate Changes to 
Gendered Body Motion Influence Basic Social Perceptions,” Lick et al. (2013) build on 
Johnson’s research by claiming that individuals, particularly queer people, may actively alter 
locomotion to exploit social assumptions. According to Lick et al. (2013), “deliberate 
modifications of gendered body motions” can affect the “basic perceptions of sex and sexual 
orientation” surmised by an observer, appending a layer of performance to Johnson’s hypothesis 



 

(p. 669). Lick et al. (2013) add that gay and lesbian subjects were particularly adept at this sort of 
gait performance, which they speculate arises from practice in social settings requiring such an 
obfuscation of identity (p. 669). Through portrayals of gait and observations of observers, 
Johnson and Lick et al. give traction to the idea that queerness is socially inferred from motion. 
         This cultural understanding of sexuality-specific and gendered movement is prevalent 
outside of psychology. Gay Twitter users have popularized and humorized the trope that gay 
men universally share the trait of speed walking (Staples, 2019). One such tweet with almost six-
thousand retweets jokes, “straight people, look behind you, chances are there's a gay person 
trying to get past because you're moving at an extremely glacial pace” (@heyDejan, 2019). 
There are also historic counterparts to these stereotypes of gay movement, which are neither 
reclamatory nor humorous. Pseudo-scientific studies of homosexuality in the late 1930s and 
early 1940s attempted to pin down the physiognomy of gay men through references to their 
locomotion (Lvovsky, 2015, p. 66). In Sex Variants: a study of homosexual patterns, psychiatrist 
George Henry (1941) noted the gay man’s “prim, affected gait” and “uncertain, mincing steps” 
(p. 283). These supposedly scientifically-backed notions of identification and signals 
emboldened post-Prohibition authorities to identify and crackdown upon bars that served gay 
customers (Lvovsky, 2015, p. 105). In 1940, investigator William Wickes discerned that the 
clientele of the Gloria Bar & Grill in New York were gay based on “a very graceful motion” of 
their hands and “a swaying movement of their hips” which one might call “a swish in show 
parlance” (Lvovsky, 2015, p. 107). From the early twentieth century to modern internet 
communities, cultural dialogues have given weight to the existence of a gay stride, whether it be 
for comic or violent means.  The veracity of gait being an indicator for gender and sexuality 
might be disputable, but it is perhaps less important than the fact that the general public assumes 
it to be so. 
  
Assumptions of Gender in Gait Recognition Development 
         Moving from human and cultural perception to computer vision, gait recognition 
technology attempts to both identify an individual and classify sets of individuals into categories. 
One area of active development is gender classification, which would enable users to assess the 
gender of any people in motion captured by cameras or other monitors. By analyzing the 
development of this software, we can ascertain assumptions of gender that underlie gait 
recognition.  

A core assumption made by software developers and computational researchers is that 
gender is binary. This prevailing notion underpins their methodologies, as they train models to 
sort data points on motion as either male or female. In “Real-Time and Robust Multiple-View 
Gender Classification Using Gait Features in Video Surveillance,” computer vision researchers 
Do et al. (2020) present a gait recognition model for gender classification in real-time, using 
surveillance footage. In sharp contrast to Johnson and Lick, little room is given in Do et al.’s 
(2020) work to atypical gender locomotion, because as they clarify, “gender classification is a 
binary classification task” (406). Do et al. are not alone in this belief. At the 12th IAPR 



 

International Conference on Biometrics, held in 2019, ten teams entered a competition to 
estimate an individual’s age and gender through wearable sensors attached to a waist-belt or a 
backpack (Ahad et al., 2020). Teams attempted to extract statistical features from the sensors 
which they then utilized to develop gender classification methods (Ahad et al., 2020). To arrive 
at classifiers, they employed a variety of deep learning methods like bi-directional long short-
term memory, temporal convolutional networks, and convolutional neural networks (Ahad et al., 
2020). One team relied on “a linear function to generate a continuous output for age estimation” 
but switched to “different non-linear activation functions to deal with the binary classification 
problem” of gender estimation (Ahad et al., 2020, p. 14). While age receives a spectrum, 
modeled through a continuous line, gender is reduced to a here-or-there approach. Another team 
created three algorithms, then for their fourth took “an average of the first three predictions” and 
marked an individual as a man if the average exceeded 0.5 (Ahad et al., 2020, p. 15). None of the 
competitors accommodate non-binary, agender, or genderqueer subjects, because as the 
aforementioned team exemplifies, the gender models recognize above 0.5 or below 0.5 and 
nothing in between. 

Gait recognition write-ups and studies are also fraught with assumptions of stability in 
gender cues from motion. Do et al. (2020) assert that “mimicking the gait of other people is 
difficult” which bolsters the value of their observations (p. 400). Huang Yongzhen, the CEO of 
Watrix, claims that “gait analysis can’t be fooled by simply limping, walking with splayed feet 
or hunching over, because we’re analyzing all the features of an entire body” (Kang, 2018, para. 
4). The implication in these statements is that gait is irreplicable and entirely unique to an 
individual, but this defies the notion established by Lick et al. that the gait of an individual may 
be transient or context-sensitive. In attempts to individualize a static characteristic, automated 
gait recognition loses sight of the dynamism involved in human gait. Thinking through gait-
based gender classifiers using Lick et al.’s research problematizes its methodology and goals, 
because surveillance cannot tell what’s legitimate from what’s performance, if there even is such 
a thing as a legitimate gait, nor can it pick up on changes in gait from a multitude of contextual 
factors. 

The final component that problematizes gait technology’s treatment of gender is its 
baseline data. In “Raw Data” is an Oxymoron, Lisa Gitelman and Virginia Jackson (2013) 
suggest that “data garner immanence in the circumstances of their imagination,” meaning data 
support conclusions precisely because of the conclusions regarding the importance of data (p. 3). 
Gitelman and Jackson (2013) propose that, despite data’s reputation of objectivity, the standards 
of data manipulation (collection, scrubbing, mining, interpretation, etc.) within a field govern its 
meaning and its capacity to substantiate claims. 

In the realm of biometric recognition, datasets of human beings (their faces or their 
motion) form the basis of machine learning algorithms, which thereby recognize and categorize 
the faces and movements of humans outside the dataset. In their examination of facial databases 
underpinning gender classification technologies, Wu et al. (2020) find that benchmark databases 
exhibit inequality in racial and gendered representation. They also underscore facial recognition 



 

systems’ incapacity to recognize those who don’t conform to normative genders, and in 
response, developed a gender-inclusive dataset that trained their model to classify non-binary 
faces with 91.97 percent accuracy (Wu et al., 2020). The central point of the exercise was that 
facial recognition systems can augment accommodation of gender minorities by actively 
constructing inclusive databases.  

Among gait recognition researchers, the preferred database is the CASIA Gait Database 
developed by the Institute of Automation, Chinese Academy of Sciences. The benchmark dataset 
contains 13,640 gait sequences gathered from 124 participants, 93 of which are men, 31 of which 
are women, and none of which identify as non-binary (Martín-Felez et al., 2010). To address this 
inequality in representation, Martín-Felez et al. (2010) claim that most studies on gait gender 
classification “have avoided dealing with its imbalanced nature by using reduced balanced 
subsets” (p. 443). Resultantly, researchers must whittle down an already small sample size, 
allowing for 31 men and 31 women to dictate the nature of their classifiers. It is in these 
miniature sample sizes that machines modeled off of the majority fail in the face of queerness. 
Gait recognition technology is therefore poised to repeat the mistakes of facial recognition, but 
its results may even be worsened given the comparative availability between images of faces and 
videos of movement. 
  
Gait Recognition at Scale and in Use: Threats to Queer Citizens 

So far, I have previewed the risk of gait recognition by taking a design-side approach, but 
the software’s detrimental effects on queer people are best understood by taking into account its 
implementation in the real world. In other words, the ways in which this technology actively 
undermines queer citizens is only visible when seeing and imagining how surveillance 
technology performs in practice––installed in street corners, checkpoints, or commercial venues. 
The potential injurious consequences of this widespread adoption to queer citizens can be 
understood in two ways. First, gait recognition technology might be accurate in assessments of 
sexual orientation, unveiling an attribute that is otherwise not immediately visible. Second, gait 
recognition can misread queer people, making public life less hospitable towards their identity. 

Given that psychologists like Johnson and Lick et al. have buttressed the idea that 
queerness is visible through movement, gait recognition could similarly target the features they 
ascribe to LGBTQ subjects. This possibility––that queer citizens could be detected and tracked 
in public settings––raises a host of ethical concerns. Debate over these ethical implications came 
alive when, in 2017, Stanford organizational behavior professor Michal Kosinski and Yilun 
Wang, a graduate student, developed an artificial intelligence program capable of detecting gay 
men and lesbian women with 81 and 71 percent accuracy respectively (Murphy, 2017).  The 
researchers employed “deep neural networks” on “35,326 facial images” gathered from dating 
apps, where profiles self-identified their sexual orientations, forming what the New York Times 
called an “A.I. gaydar” (Wang and Kosinski, 2018, p. 2) (Murphy, 2017). The study drew ire 
from LGBTQ advocacy organizations GLAAD and the Human Rights Campaign, who criticized 
the study’s exclusively white sample and reduced the research to a description of “beauty 



 

standards on dating sites that ignores huge segments of the LGBTQ community” (“GLAAD,” 
para. 2). They further that the study invokes a potential harm to “both heterosexuals who are 
inaccurately outed, as well as gay and lesbian people who are in situations where coming out is 
dangerous” (“GLAAD,” para. 3). If gait technologies enable an explicit or implicit tracking of 
queer populations (which Johnson and Lick et al.’s research suggest they could), they would 
entail the consequences of Kosinski and Wang’s defunct prototype, bringing forth the identities 
of the surveilled and endangering them in the process. 

Misclassification and failures in gait recognition pose another unique threat to LGBTQ 
people, particularly gender minorities. A team of information science researchers at the 
University of Colorado, led by Morgan Scheuerman, examined how computer vision services for 
gender classification and gender labeling handle cases of gender diversity. The team found that 
services provided by IBM, Amazon, Microsoft and Clarifai misidentified pictures of trans men 
as women 29.5 percent of the time and pictures of trans women as men 12.7 percent of the time 
(Scheuerman et al., 2019). Conversely, cisgender women had an accuracy rate of 98.3 percent 
and cisgender men received correct classifiers 97.6 percent of the time (Scheuerman et al., 2019). 
None of the commercially-available facial analysis services enable non-binary categorization, 
meaning agender and non-binary subjects were always misrecognized (Scheuerman et al., 2019). 
Once again, these risks of visual recognition translate to identifying people’s motion. As 
aforementioned, the models for gender classification through gait recognition rely on binary 
assignments, and are therefore prone to perpetuate the mistakes of facial recognition in 
misidentifying non-binary people. Likewise, trans people will continue to be vulnerable under 
gait recognition software for the same reasons they are under facial recognition: limited samples 
that tend to the cisgender majority are used to construct models. These slip-ups propagate hostile 
environments; for instance, because of misidentification, trans people face “invasive body 
searches or harassment if their ID does not match their gender” (Millar, 2019, para. 9). Hence, 
gait recognition software, by lacking nuance in its analysis of gender, can promote hostility in 
the public spaces that it occupies. 

The sites of surveillance, which tend to be securitized zones, are also critical in 
understanding the dormant danger of gait biometrics for queer citizens. With regard to police 
departments, law enforcement has a history of hostility towards LGBTQ citizens which persists 
today. A report by UCLA’s Williams Institute on law enforcement’s mistreatment of LGBTQ 
Americans summarizes the historical criminalization of queerness by pointing to bar raids (like 
the aforementioned one at the Gloria Bar & Grill) which spurred the Stonewall Riots and anti-
sodomy laws enforced by police (Mallory et al., 2015). The report goes on to cite a 2013 survey 
of LGBT violence survivors wherein 48 percent reported experiences of police misconduct as 
well as a 2011 survey of trans Americans where 22 percent reported harassment from law 
enforcement (Mallory et al., 2015). Notably, up until its repeal in February 2021, a 1976 law 
known as the “walking while trans” ban in New York City allowed for the profiling of trans 
women by conflating them with prostitutes based on their attire or “innocuous” behavior 
(McKinley and Ferré-Sadurní, 2021, para. 17). As gait recognition technology identifies 



 

irregularity in gendered movement, it may open the door for targeted enforcement against queer 
people, who already face disproportionate persecution from police. In essence, algorithms run 
with surveillance footage take the place of profiling officers. 

The TSA, one of the earliest adoptees of facial recognition technologies in the United 
States, has also historically harassed and invalidated trans people. Toby Beauchamp (2009) 
writes that in the wake of 9/11, the adoption of the Real ID Act subjected trans people to more 
frequent stops at airport security given mismatches in listed genders on their various forms of 
identification. Moreover, Beauchamp (2009) references a 2003 DHS Advisory, which reads 
“male bombers may dress as females in order to discourage scrutiny,” ultimately conveying that 
non-normative gender presentations represent a security threat to the state (p. 356). In 
Securitizing Gender: Identity, Biometrics, and Transgender Bodies at the Airport, Paisley Currah 
and Tara Mulqueen (2011) further that the rise in whole body scanners at airports has heightened 
the scrutiny of trans bodies, requiring trans travelers to “contort their gendered selves to appear 
as conventionally gendered as possible at the airport” (p. 573). Implementation of gait 
technology would amplify such dysphoria-inducing and mobility-restraining encounters, 
flagging travelers whose styles of locomotion don’t comport with normative expectations. 
  
Conclusion 

In light of its flaws in methodology, its probable expansion, and its deployment in 
security settings, gait recognition carries significant consequences for queer people, be it through 
misrecognition or targeted identification. Whether or not governments will heed the warnings of 
queer rights organizations remains unknown. At any rate, gait recognition technology invigorates 
an age-old debate weighing security solutions against their implications for queer citizens. In that 
same manner, machine learning amplifies and exposes the shortsightedness of its creators, 
demonstrating that without an eye for inclusion, technology can reify systems of oppression. 

 
 

 

 

 

 

 

 

 

 



 

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