3-Parameter Hough Ellipse Detection Algorithm for Accurate Location of Human Eyes

Accurately positioning the Human Eyes plays an important role in the detection of the fatigue driving. In order to improve the performance of positioning of human face and eyes, an accurately positioning method of the human eyes is proposed based on the 3-parameter Hough ellipse detection. Firstly, the human face area is divided by using the skin color clustering and segmentation algorithm. Then, the segmented image is filtered by using its geometric structure and the approximate positions of the human face area and eyes are calculated. Finally, on the basis of the spinning cone-shape eye model, the position of human face and eyes is accurately determined by using the 3-parameter Hough transformation ellipse detection algorithm. The different images of human face are used to test the performance of the proposed method. The experimental results show that the extreme value of upper and lower eyelids and the actual position is 0.104 and the proposed algorithm has higher positioning accurac

Hybrid Particle and Kalman Filtering for Pupil Tracking in Active IR Illumination Gaze Tracking System

A novel pupil tracking method is proposed by combining particle filtering and Kalman filtering for the fast and accurate detection of pupil target in an active infrared source gaze tracking system. Firstly, we utilize particle filtering to track pupil in synthesis triple-channel color map (STCCM) for the fast detection and develop a comprehensive pupil motion model to conduct and analyze the randomness of pupil motion. Moreover, we built a pupil observational model based on the similarity measurement with generated histogram to improve the credibility of particle weights. Particle filtering can detect pupil region in adjacent frames rapidly. Secondly, we adopted Kalman filtering to estimate the pupil parameters more precisely. The state transitional equation of the Kalman filtering is determined by the particle filtering estimation, and the observation of the Kalman filtering is dependent on the detected pupil parameters in the corresponding region of difference images estimated by particle filtering. Tracking results of Kalman filtering are the final pupil target parameters. Experimental results demonstrated the effectiveness and feasibility of this method.Published versio

Calibration-Free Eye Gaze Direction Detection with Gaussian Processes

In this paper we present a solution for eye gaze detection from a wireless head mounted camera designed for children aged between 6 months and 18 months. Due to the constraints of working with very young children, the system does not seek to be as accurate as other state-of-the-art eye trackers, however it requires no calibration process from the wearer. Gaussian Process Regression and Support Vector Machines are used to analyse the raw pixel data from the video input and return an estimate of the child's gaze direction. A confidence map is used to determine the accuracy the system can expect for each coordinate on the image. The best accuracy so far obtained by the system is 2.34$^{circ}$ on adult subjects, tests with children remain to be done

Tecnicas de seguimiento de caracteristicas faciales en secuencias de imágenes basadas en metodos de libre modelo.

Las características faciales juegan un papel importante en el desarrollo de sistemas basados en visión por computador para diferentes aplicaciones como son: la interacción hombre maquina, reconocimiento automático de expresiones faciales o también la identificación de la fatiga en conductores de automóviles, dentro de estos sistemas, el seguimiento de características faciales es una de las etapas que necesitan ser desarrolladas, por lo tanto, este documento se centra en la revisión de las diferentes técnicas para el seguimiento de características faciales, como son métodos de libre modelo (filtro de Kalman, filtro de partículas, entre otras)

Tecnicas de seguimiento de caracteristicas faciales en secuencias de imágenes basadas en metodos de libre modelo.

Las características faciales juegan un papel importante en el desarrollo de sistemas basados en visión por computador para diferentes aplicaciones como son: la interacción hombre maquina, reconocimiento automático de expresiones faciales o también la identificación de la fatiga en conductores de automóviles, dentro de estos sistemas, el seguimiento de características faciales es una de las etapas que necesitan ser desarrolladas, por lo tanto, este documento se centra en la revisión de las diferentes técnicas para el seguimiento de características faciales, como son métodos de libre modelo (filtro de Kalman, filtro de partículas, entre otras)

RCEA-360VR: Real-time, continuous emotion annotation in 360◦ VR videos for collecting precise viewport-dependent ground truth labels

Precise emotion ground truth labels for 360◦ virtual reality (VR) video watching are essential for fne-grained predictions under varying viewing behavior. However, current annotation techniques either rely on post-stimulus discrete self-reports, or real-time, con- tinuous emotion annotations (RCEA) but only for desktop/mobile settings. We present RCEA for 360◦ VR videos (RCEA-360VR), where we evaluate in a controlled study (N=32) the usability of two peripheral visualization techniques: HaloLight and DotSize. We furthermore develop a method that considers head movements when fusing labels. Using physiological, behavioral, and subjective measures, we show that (1) both techniques do not increase users’ workload, sickness, nor break presence (2) our continuous valence and arousal annotations are consistent with discrete within-VR and original stimuli ratings (3) users exhibit high similarity in viewing behavior, where fused ratings perfectly align with intended labels. Our work contributes usable and efective techniques for collecting fne-grained viewport-dependent emotion labels in 360◦ VR