Object detection with a minimal set of examples using Convolutional PCA
Current object detection systems reach high detection rates, at the expense of requiring a large training database. This paper presents a new method for object detection, that gives state-of-the-art results, while using a reduced training database. The proposed system relies on a new local feature extraction approach inspired by Convolutional Neural Networks, Principal Component Analysis and Multilayer Perceptrons. We show that the proposed scheme improves robustness and generalization on the specific problem of face detection, with a very reduced set of exemplar face images.