GazeScor­er — Auto­mat­ed gaze direc­tion scor­ing from videos col­lect­ed online through con­ven­tion­al webcam.

Alex Fras­er, Uni­ver­si­ty of Oxford

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As online research has become more preva­lent, researchers are inves­ti­gat­ing the pos­si­bil­i­ty of repli­cat­ing tech­niques that go beyond sim­ple behav­iour­al mea­sure­ments. One method that has cap­tured the imag­i­na­tion of researchers is lever­ag­ing the web­cam to col­lect eye-track­ing data. Sev­er­al pack­ages have been devel­oped for col­lect­ing such data but have sig­nif­i­cant lim­i­ta­tions due to exten­sive and poten­tial­ly frus­trat­ing cal­i­bra­tion pro­ce­dures. Unfor­tu­nate­ly, this can lim­it the acces­si­bil­i­ty of these pack­ages when col­lect­ing data with spe­cif­ic pop­u­la­tions, such as chil­dren and par­tic­i­pants with neu­ro-devel­op­men­tal dif­fi­cul­ties. To over­come this, we have looked at how gaze detec­tion stud­ies are con­duct­ed with infants, where researchers will man­u­al­ly score gaze direc­tion from videos to min­imise data loss. 

Using these meth­ods, we have devel­oped GazeScor­er, an auto­mat­ed gaze scor­ing pack­age that can dis­tin­guish a Left, Right, and Cen­tral gaze loca­tion using basic image pro­cess­ing. Using videos col­lect­ed through a Goril­la-host­ed exper­i­ment, we have demon­strat­ed a good lev­el of inter-rater reli­a­bil­i­ty between GazeScor­er and a man­u­al scor­er. This opens the pos­si­bil­i­ty of a hybrid-scor­ing sys­tem with min­i­mal man­u­al inter­ven­tion in the short term. Future devel­op­ment will focus on util­is­ing live web­cam footage for data col­lec­tion through the brows­er. This soft­ware would pro­vide a poten­tial resource for researchers who would ben­e­fit from gaze-based respons­es, but do not require high spa­tial resolution.

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GazeScor­er — Auto­mat­ed gaze direc­tion scor­ing from videos col­lect­ed online through con­ven­tion­al webcam.