A fast face detection architecture for auto-focus in smart-phones and digital cameras

Abstract

Auto-focus is very important for capturing sharp human face centered images in digital and smart phone cameras. With the development of image sensor technology, these cameras support more and more highresolution images to be processed. Currently it is difficult to support fast auto-focus at low power consumption on high-resolution images. This work proposes an efficient architecture for an AdaBoost-based face-priority auto-focus. The architecture supports block-based integral image computation to improve the processing speed on high-resolution images; meanwhile, it is reconfigurable so that it enables the sub-window adaptive cascade classification, which greatly improves the processing speed and reduces power consumption. Experimental results show that 96% detection rate in average and 58 fps (frame per second) detection speed are achieved for the 1080p (1920×1080) images. Compared with the state-of-the-art work, the detection speed is greatly improved and power consumption is largely reduced.

创新点

1. 提出了并行的阵列化计算架构, 该架构支持包括高分辨率图上的基于块的积分处理, 从而实现并行计算, 可以加速人脸检测中积分计算过程。

2. 提出了子窗口自适应的计算机制, 该机制可以在计算量和检测精度方面达到一个比较好的权衡。

3. 提出了可重构的架构计算机制, 通过阵列之间互联模式重构, 阵列内部基本计算单元计算模式重构, 以及基本计算单元功能重构, 来支持子窗口自适应的分类计算, 有效减少计算量, 提高计算性能。