Tobias Ruf

Face Detection with the Sophisticated High-speed Object Recognition Engine (SHORE)

An approach enabling the detection, tracking and fine analysis (e.g. gender and facial expression classification) of faces using a single web camera is described. One focus of the contribution lies in the description of the concept of a framework (the so-called Sophisticated High-speed Object Recognition Engine – SHORE), designed in order to create a flexible environment for varying detection tasks. The functionality and the setup of the framework are described, and a coarse overview about the algorithms used for the classification tasks will be given. Benchmark results have been obtained on both, standard and publicly available face data sets. Even though the framework has been designed for general object recognition tasks, the focus of this contribution lies in the field of face detection and facial analysis. In addition a demonstration application based on the described framework is given to show analysis of still images, movies or video streams.

Towards Robust Real-Time Valence Recognition from Facial Expressions for Market Research Applications

A novel system for facial expression recognition is presented. It is designed to assess valence (positive or negative feeling) in the context of market research applications. Other design parameters that were met are real-time capability and robust results even when using off-the-shelf equipment (standard web cam and lighting). The paper further presents an empirical study that attests the system satisfying predictive validity for market research applications as long as very small absolute valence values close to zero are not interpreted.

CENSUS HISTOGRAMS: A SIMPLE FEATURE EXTRACTION AND MATCHING APPROACH FOR FACE RECOGNITION

Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in...

Check my chart: a robust color chart tracker for colorimetric camera calibration

We present an algorithm to detect and track color calibration charts in images robustly. A model of the calibration chart describes the geometry of the chart regions by polygons along with the related reference colors. A homography maps the model into the image and an optimizer adapts the mapping parameters to align the model regions to the chart in the image. We propose a cost function that evaluates the quality of model alignment based on color statistics and an efficient method to extract color statistics in polygonal image regions using the integral image. The algorithm measures the colors of the chart in the image and determines correction parameters. Without loss of generality, we describe the method using the X-Rite ColorChecker Classic as a typical chart. Experiments show the robustness to noise and blur and the real-time capability of the system.