# Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations
## 1 Introduction  
Biometrics encompasses the science of measuring individual body characteristics in order to distinguish a person among many others (hard biometrics). The first idea of identifying people based on biometrics dates back at the end of the 19 th century with the seminal works in [1, 2]. Those works encompass the application of a scientific approach based on different traits that are nowadays classified and recognized as soft biometrics: in facts, the considered traits were the color of the eyes, shape and, size of the head, height, weight, scars, and tattoos. Soft biometrics are defined indeed in terms of characteristics that provide information about the individual but without the distinctiveness and permanence able to discriminate between any two individuals [3]. They can be categorized as physical, behavioral, or adhered human characteristics corresponding to pre-defined human compliant categories [4]. Compared with hard biometrics, these traits present stronger invariance [5] and they can be often extracted without requiring subject cooperation and from low-quality data [6]. Moreover, they can complement and strengthen hard primary biometric identifiers, since are established and time-proven by humans in order to differentiate their peers; as a consequence, one of the most successful and investigated solution is to strengthen classic biometric identification schemes [7, 8, 9]. Refer to [10] for a complete review. All of the aforementioned reasons make soft biometrics veryappealing, as shown by the numerous works present in the literature, spacing from authentication [11] to customized robot behavior [12], from social networks users analysis [13] to healthcare [14]. On the other hand, the gaze represents one of the most investigated human traits since it expresses human emotions, feelings, and intentions. Recently, the possibility to consider gaze as a possible additional soft biometric trait has obtained a lot of attention from the scientific community. It is well known that there is a close relationship between what the eyes are gazing at and what the mind is engaged with, and this idea was formulated by [PERSON] and [PERSON] in the \"Eye-Mind Hypothesis\" [15]. This evidence has been recently proved by using eye-tracker technology [16]. Unfortunately, such systems are usually expensive and invasive (in case of setup based on wearable helmet or glasses). Moreover, they work under constraints like head movement and/or distance from the target, often requiring the user cooperation during a time-consuming calibration procedure. The above drawbacks project a bias in the acquired data since behavioral patterns become not natural, but in some way adapted to respect the imposed constraints [17]. Exploratory studies under unconstrained scenarios have been recently carried out in [18; 19; 20], but they used either static images or few and very generic videos as stimuli, making difficult to draw settled conclusions. The motivations of this work can be then found in the lacks of systematic studies on clinically approved stimuli to provide evidence of the correlation between exploratory paths and individual identities in natural scenarios (without calibration, imposed constraints, wearable tools). In this work, evidence of the natural correlation between the user's scene exploration and soft biometric identification is provided. This is achieved by acquisition sessions in which different users watch visual stimuli on a screen. A consumer camera, pointing towards the user, is employed and the acquisition outcomes are given as input to an innovative computer vision pipeline that extracts the user gaze. The system does not require user calibration nor impose constraints in terms of appearance, hairstyle, beard, eyeglasses, etc. The camera has been integrated into the scene in an ecological setting. Individuals were informed that a camera would record them but did not know the purpose of scientific research to minimize bias on visual exploration behaviors.