Robust User Identification Based on Facial Action Units Unaffected by Users' Emotions

Abstract

We report on promising results concerning the identification of a user just based on its facial action units. The related Random Forests classifier which analyzed facial action unit activity captured by an ordinary webcam achieved very good values for accuracy (97.24 percent) and specificity (99.92 percent). In combination with a PIN request the degree of specificity raised to over 99.999 percent. The proposed biometrical method is unaffected by a user's emotions, easy to use, cost efficient, non-invasive, and contact-free and can be used in human-machine interaction as well as in secure access control systems.

High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method

“…Bernice Projesz et al [24] explain that despite the good results with the thetaband, the beta frequency has become a strong indicator of alcoholism among scientists and medical professionals. However, the results of Wajid Mumtaz et al [10] see a good classifier of alcoholics and non-alcoholics in the thetaband and the Hi-Gamma bands (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). Eveline A. de Bruin et al [26] analyze the EEG data of heavy drinking students compared to light drinking students and also comes to the conclusion that the EEG data of heavy drinking students, especially in theta and Gamma band, differ enormously from the EEG data of the control group.…”

“…In addition, in future work we will transfer this novel pre-processing step (unfolding outdated standard bandwidths in fine-graded spectrums) prior to entering a Random Forests classifier to other applications outside EEG, such as pupillary hippus for user performance and cognitive load assessment [33][34][35], and frequencies of facial actions for cognitive load evaluation [36].…”

Development of a Highly Precise Place Recognition Module for Effective Human-robot Interactions in Changing Lighting and Viewpoint Conditions

“…• to triangulate objective and perceived user-oriented concepts [65,66,67] using physiological sensor data (i.e., electroencephalographic data [68,69,70,71] and spectra [72,73,74], electrocardiographic data [75,76], electrodermal activity [77], eye fixation [78,79,56], eye pupil diameter [80,81,53,82], facial expressions [83]), and, Page 569…”

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

“…Researchers have successfully applied the RF method to various research problems such as brain imaging (Kačar et al, 2011), gene expression (Díaz-Uriarte & de Andrés, 2006), biomarker identification (Zhang et al, 2008), psychometry (Sauer, Lemke, Zinn, & Buettner, 2015;Sauer et al, 2018), and, recently, to IS problems (Ali, Khan, Ahmad, & Maqsood, 2012;Buettner, 2016dBuettner, , 2018. In particular, the RF method is especially useful in, but not limited to, "small n, large p" problems where the number of predictor variables p is larger than the number of cases n. Even with sufficiently large samples, the RF method can be a valuable tool because it allows the delineation of statistical properties such as non-linear trends, high-degree interaction, and correlated predictors.…”

“…Scholars have found physiological markers of mental effort in EEG (Fairclough, Venable, & Tattersall, 2005;Wang, Gwizdka, & Chaovalitwongse, 2016;Buettner, 2017b), fMRI (Lim et al, 2010;Gwizdka, 2013b), fNIR (Sassaroli et al, 2008;Herff et al, 2014), EDA (Wilson, 2002;Boucsein, 2012;Buettner, 2017b), HR (Vogt, Hagemann, & Kastner, 2006;Brookhuis & de Waard, 2010), facial action (Buettner, 2017b(Buettner, , 2018, fEMG (Stone & Wei, 2011;Ekman, Friesen, & Hager, 2002), PET (Kramer, 1990;Just, Carpenter, & Miyake, 2003), MEG (Tanaka, Ishii, & Watanabe, 2015;Ishii, Tanaka, & Watanabe, 2016), and various eye-tracking measures (e.g., Rayner, 1998;Buettner, 2013Buettner, , 2017bGwizdka, 2016). In particular, they have found that mental effort is associated with a lot of eye-related characteristics, such as a user's pupil diameter (Hess & Polt, 1964;Beatty, 1982;Buettner, 2017b), eye-blink duration and blink rate (Fairclough et al, 2005;Marshall, 2007), eye saccade speed (Porter et al, 2010;Buettner, 2013Buettner, , 2017b, and the number of eye gaze fixations (Rayner, 1998;Buettner, 2013Buettner, , 2017b.…”