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Biometric technologies are considered to be a more convenient and secure way of user authentication, as compared to traditional identification and verification methods (ID, cards or passwords). It is well known that human biometric identifiers can be divided into two categories: physiological biometrics, which may relate to parts of the body (face, ear, palm, fingerprint, iris), and behavioral biometrics including voice, signature, handwriting and gait (Gavrilova, & Monowar, 2012). There is also an emerging area of biometric domain, that uses social on-line interactions and aesthetics for biometric recognition (Sultana, Paul, & Gavrilova, 2017; Azam, & Gavrilova, 2017). Some biometric identifiers require person cooperation because it is difficult to obtain a person’s face, fingerprint, iris or voice from a location (Munsell, Tumlyakov, & Wang, 2017). Some others biometric identifiers require high quality of an image or a video for accurate person identification and feature extraction. Gait is one of the few biometric identifiers where person cooperation is not needed, and person can be identified using even a low-quality image or a video (Das, Guang, & Cheng-Tsun, 2014). Thus, the recent popularity of gait biometric can be attributed to its unobtrusiveness, universality and non-vulnerability in the case of a spoof attack, as gait is difficult to hide or imitate. As a result, gait analysis has been conducted in various applications such as video surveillance systems (Cucchiara,Grana, Pretti, & Vezzani, 2005), 3D human body modeling (Bae, & Park, 2013), forensic science (Bouchrika, Goffredo, Carter, & Nixon, 2011), and elderly population health assessment (Kressing, & Beauchet, 2006).
Gait can not only define the way a person walks, but also provides interesting cues on individuals daily routine, mental state, health state or even cognitive function (Wang, 2011). The importance of incorporating cognitive behavior and analysis in biometric systems has been noted recently (Wang, Widrow, Zadeh, Howard, Wood, Chan, & Gavrilova, 2016). One of the early works conducted in BT lab demonstrated that combining auxiliary cues from the gait videos in addition to extracting traditional gait features can significantly enhance the accuracy of subject identification (Bazazian, & Gavrilova, 2015). An interesting link between gait and cognition has been observed recently (Wang, Fariello, Gavrilova, Kinsner, & Shell, 2013). Normally, gait and cognitive function of a person are being evaluated independently; however, the strong correlation between changes in walking style, for instance, and some mental illnesses or cognitive impairments have been noted and investigated (Montero-Obasso, Verghese, Beauchet, & Hausdroff, 2012). Gait skeletal information can not only be used for person identification, but also for other applications, including gender detection (Kastaniotis, Theodorakopoulos, Theoharatos, Economou, & Fotopoulos, 2015). It can also be used to recognize = actions of a person from recorded sequences or in real-time (Ahmed, Paul, & Gavrilova, 2016).