Learning Object Detection from a Small Number of Examples: The Importance of Good Features

Face detection systems have recently achieved high detection rates[11, 8, 5] and real-time performance[11]. However, these methods usually rely on a huge training database (around ????????? positive examples for good performance). While such huge databases may be feasible for building a system that detects a single object, it is obviously problematic for scenarios where multiple objects (or multiple views of a single object) need to be detected. Indeed, even for multiview face detection the performance of existing systems is far from satisfactory. In this work we focus on the problem of learning to detect objects from a small training database. We show that performance depends crucially on the features that are used to represent the objects. Specifically, we show that using local edge orientation histograms (EOH) as features can significantly improve performance compared to the standard linear features used in existing systems. For frontal faces, local orientation histograms enable st...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Kobi Levi, Yair Weiss
• Keywords: Computer Vision, CVPR 2004, Edge Orientation Histograms, Face Detection Systems, Histograms Enable State, Local Orientation Histograms, Multiview Face Detection