Square Patch Feature: Faster weak-classifier for robust object detection

This paper presents a novel generic weak classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other weak classifiers shows improvement. Each weak classifier is based on the difference between two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the weak classifiers computation. The computation requires fewer arithmetic operations and fewer accesses to the main memory in comparison to the well known Viola-Jones Haar-like classifier. In addition to the faster computation, the weak classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature classifier is as accurate as the Viola-Jones Haar-like classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster.