Mr. Justin M. Hnatow Profile

Justin M. Hnatow

Image Analysis Software Engineer at Hand Held Products Inc

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Proceedings Article | 30 April 2007 Paper

A multi-camera system for real-time pose estimation

Andreas Savakis, Matthew Erhard, James Schimmel, Justin Hnatow

Proceedings Volume 6560, 656006 (2007) https://doi.org/10.1117/12.719633

KEYWORDS: Cameras, Facial recognition systems, Mouth, Head, Scalable video coding, Sensors, Error analysis, Video surveillance, Video, Imaging systems

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This paper presents a multi-camera system that performs face detection and pose estimation in real-time and may be used for intelligent computing within a visual sensor network for surveillance or human-computer interaction. The system consists of a Scene View Camera (SVC), which operates at a fixed zoom level, and an Object View Camera (OVC), which continuously adjusts its zoom level to match objects of interest. The SVC is set to survey the whole filed of view. Once a region has been identified by the SVC as a potential object of interest, e.g. a face, the OVC zooms in to locate specific features. In this system, face candidate regions are selected based on skin color and face detection is accomplished using a Support Vector Machine classifier. The locations of the eyes and mouth are detected inside the face region using neural network feature detectors. Pose estimation is performed based on a geometrical model, where the head is modeled as a spherical object that rotates upon the vertical axis. The triangle formed by the mouth and eyes defines a vertical plane that intersects the head sphere. By projecting the eyes-mouth triangle onto a two dimensional viewing plane, equations were obtained that describe the change in its angles as the yaw pose angle increases. These equations are then combined and used for efficient pose estimation. The system achieves real-time performance for live video input. Testing results assessing system performance are presented for both still images and video.

Proceedings Article | 12 April 2007 Paper

An eye model for uncalibrated eye gaze estimation under variable head pose

Justin Hnatow, Andreas Savakis

Proceedings Volume 6539, 65390Q (2007) https://doi.org/10.1117/12.720834

KEYWORDS: Eye, Eye models, Head, Iris recognition, Calibration, Systems modeling, Optical spheres, Zoom lenses, Computing systems, Image resolution

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Gaze estimation is an important component of computer vision systems that monitor human activity for surveillance, human-computer interaction, and various other applications including iris recognition. Gaze estimation methods are particularly valuable when they are non-intrusive, do not require calibration, and generalize well across users. This paper presents a novel eye model that is employed for efficiently performing uncalibrated eye gaze estimation. The proposed eye model was constructed from a geometric simplification of the eye and anthropometric data about eye feature sizes in order to circumvent the requirement of calibration procedures for each individual user. The positions of the two eye corners and the midpupil, the distance between the two eye corners, and the radius of the eye sphere are required for gaze angle calculation. The locations of the eye corners and midpupil are estimated via processing following eye detection, and the remaining parameters are obtained from anthropometric data. This eye model is easily extended to estimating eye gaze under variable head pose. The eye model was tested on still images of subjects at frontal pose (0^o) and side pose (34^o). An upper bound of the model's performance was obtained by manually selecting the eye feature locations. The resulting average absolute error was 2.98^o for frontal pose and 2.87^o for side pose. The error was consistent across subjects, which indicates that good generalization was obtained. This level of performance compares well with other gaze estimation systems that utilize a calibration procedure to measure eye features.

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