Abstract—Real-time human eye recognition and tracking systems with human-computer interaction mechanism are being adopted to advance user experience in smart devices and consumer electronic systems. Eye tracking systems measure eye gaze and pupil response non-intrusively. This paper presents an analysis of pupil response to video structure and content. A consumer device that can assess user response to a video can provide better experiences with approaches such as real-time content adaptation. The first set of experiments involved presenting different video content to subjects and measuring eye response with an eye tracker. Results show that pupil constrictions and magnitude of constrictions vary with content. Significant changes in video and scene cuts led to sharp constrictions. User response to videos can provide insights that can improve subjective quality assessment metrics. This paper also presents an analysis of the pupillary response to quality changes in videos with the second set of experiments. The results of the tests show pupil constrictions for noticeable changes in perceived quality. Using real-time eye tracking systems for video analysis and quality evaluation can open a new class of applications for consumer electronic systems. Index Terms—Eye Tracking, Pupillary Response, Video Analysis, Video Quality Evaluation I. INTRODUCTION ESEARCH on eye response in human-computer interaction has recently gained much attention. Recent explorations have introduced vision-based interactions in commercial applications such as virtual gaming and consumer electronics systems [1]. Vision, touch, and voice are replacing traditional interfaces for interaction with communications and entertainment devices [2]. Quality of experience for such consumer electronic products defines their success rate [3]. Subjective measures to evaluate the Quality of Experience (QoE) do exist. However, the difficulty of conducting evaluations with human subjects, and possible bias because of individual skills and experiences, make such assessments difficult to perform and repeat. Automating these evaluations Manuscript received October 1, 2017; accepted November 15, 2017. Date of publication December 19, 2017. (Corresponding author: D. Pappusetty.) D. Pappusetty and H. Kalva are with the Department of Computer & Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL 33431 USA (e-mail: {dpappuse, hkalva}@fau.edu). H. S. Hock is with the Department of Psychology, Florida Atlantic University, Boca Raton, FL 33431 USA (e-mail: hockhs@fau.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TCE.2017.015109 using eye trackers can significantly improve current methods for QoE assessments. A few recent studies have explored the use of eye trackers in consumer electronic systems [4]. Gaze research is primarily limited to device control in consumer electronic systems [5]. Wearable eye tracking as an alternative to tracking embedded in consumer electronic devices will enable new user applications [6]. This paper presents another application of eye trackers in consumer electronic devices – using pupil response to assess video content and quality. Studies of human visual perception have been conducted for over 100 years and have led to an abundant knowledge and thorough understanding of the Human Visual System (HVS). The functions and behaviors of the HVS have extensive applications in the field of image processing, computer vision, and video processing [7]. The findings from the studies of the HVS can be utilized to develop efficient, perceptually optimized video processing frameworks, algorithms, and consumer electronic applications [8]. In a human eye, the iris is the colored part at the center of the eye and pupil is the opening at the center of the iris. The size of the opening determines the amount of light incident on the retina. Two smooth iris muscles, the sphincter pupillae and the radial pupillae, control pupil diameter by their relative activity [9]. The radial muscles are used to dilate the pupil, and the sphincter muscles contract the pupil. Pupil constricts to intense light and dilates with dim light [10]. The pupil of the human eye can constrict to 1.5 mm in diameter and can enlarge to about 8 to 9 mm. It can react to stimuli as quick as 0.2s and peak to 0.5s to 1.0s. [11]. Pupillometry denotes to the measurement of variations in the diameter of the pupillary aperture of the eye. Recent studies have shown that the pupil response is far more than a light reflex [12]. Pupil changes provide insights into behavioral responses of individuals. For example, a pupil dilates with pleasant stimuli and constricts with unpleasant ones [13]. A strong emotional stimulus producing a dilation can also override the pupillary constriction due to intense light stimuli [14]. Also, in the absence of a bright light, pupil responds even if one is aware of the stimulus, whether one is paying attention to it, or even if one is thinking about it [15]. Beatty coined the term Task-invoked pupillary response (TEPR) [16] [17]. TEPR is a pupillary response caused by a cognitive load imposed on a human. Jang et al. presented a system to detect a driver’s lane change intent using pupil response [18]. Tryon listed 23 factors that influence pupil size [19]. Pupil Response to Quality and Content Transitions in Videos Deepti Pappusetty, Student Member, IEEE, Hari Kalva, Senior Member, IEEE, and Howard S. Hock R 410 IEEE Transactions on Consumer Electronics, Vol. 63, No. 4, November 2017 0098 3063/17/$20.00 © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.