Gender Classification Based on Eye Movements: A Processing Effect During Passive Face Viewing

Abstract

Studies have revealed superior face recognition skills in females, partially due to their different eye movement strategies when encoding faces. In the current study, we utilized these slight but important differences and proposed a model that estimates the gender of the viewers and classifies them into two subgroups, males and females. An eye tracker recorded participant’s eye movements while they viewed images of faces. Regions of interest (ROIs) were defined for each face. Results showed that the gender dissimilarity in eye movements was not due to differences in frequency of fixations in the ROI s per se. Instead, it was caused by dissimilarity in saccade paths between the ROIs. The difference enhanced when saccades were towards the eyes. Females showed significant increase in transitions from other ROI s to the eyes. Consequently, the extraction of temporal transient information of saccade paths through a transition probability matrix, similar to a first order Markov chain model, significantly improved the accuracy of the gender classification results.

Privacy-aware eye tracking using differential privacy

“…More closely related to our work, several researchers have shown that gender and age can be inferred from eye movements, e.g. by analysing the spatial distribution of gaze on images like faces [Cantoni et al 2015;Sammaknejad et al 2017].…”

Predicting artificial visual field losses: A gaze-based inference study

“…Indeed, cognitive processes can be observed through eye movements and offer a wealth of information related to internal processes (Itti, 2015;Coutrot, Hsiao, & Chan, 2018). Inference from gaze data consists in deducing subjective characteristics solely from ocular data, such as age (Le Meur et al, 2017b), gender (Coutrot, Binetti, Harrison, Mareschal, & Johnston, 2016;Sammaknejad, Pouretemad, Eslahchi, Salahirad, & Alinejad, 2017), mental states and traits (Liao, Zhang, Zhu, & Ji, 2005;Hoppe, Loetscher, Morey, & Bulling, 2015;Yamada & Kobayashi, 2017;Hoppe, Loetscher, Morey, & Bulling, 2018), expertise and skill proficiency (Eivazi & Bednarik, 2011;Boccignone, Ferraro, Crespi, Robino, & de'Sperati, 2014;Tien et al, 2014;Kolodziej, Majkowski, Francuz, Rak, & Augustynowicz, 2018), and neurological disorders (Kupas, Harangi, Czifra, & Andrassy, 2017;Terao, Fukuda, & Hikosaka, 2017).It has proven useful in identifying autism spectrum disorder (Pierce et al, 2016), fetal alcohol spectrum disorder (Tseng, Paolozza, Munoz, Reynolds, & Itti, 2013), dementia (Zhang et al, 2016;Beltrán, García-Vázquez, Benois-Pineau, Gutierrez-Robledo, & Dartigues, 2018), dyslexia (Benfatto et al, 2016), anxiety (Abbott, Shirali, Haws, & Lack, 2017), mental fatigue (Yamada & Kobayashi, 2017), and other disorders. It has also been applied to task detection (Borji & Itti, 2014;Haji-Abolhassani & Clark, 2014;Kanan, Ray, Bseiso, Hsiao, & Cottrell, 2014;Boisvert & Bruce, 2016).…”

“…We selected these two types of models for their dissimilarities as classifiers, which may lead us to learn separate information about our experimental data. Markov models have shown their effectiveness when applied to gaze data (e.g., Simola, Salojärvi, & Kojo, 2008;Kanan, Bseiso, Ray, Hsiao, & Cottrell, 2015;Coutrot et al, 2016;Rai et al, 2016;Sammaknejad et al, 2017). They have been extensively used for modeling time series in general (Camastra & Vinciarelli, 2008).…”

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

“…It has been pointed out that eye tracking data can even be associated with mental disorders, such as Alzheimer's disease [23], Parkinson's disease [30], and schizophrenia [17]. Furthermore, eye tracking data holds rich personal information, including personality traits [18], gender [50], and user identity [8].…”