Real-time face-priority auto focus for digital and cell-phone cameras

Auto-focus (AF) has been a key feature in consumer level digital and cell-phone cameras allowing users to focus automatically on a particular plane in a scene in order to get a sharp image. Face priority AF has become of interest lately due to the fact that most pictures captured by consumers are of human faces. In face-priority AF, the focusing decision is made based on a detected face area in the image, thus capturing a sharp picture of the face. While many face detection algorithms exist in the literature, very few of them are actually suitable for real-time software deployment on resource limited digital or cell-phone camera processors. In this paper, a fast face-detection algorithm is introduced by combining a skin color model cluster with a computationally efficient shape processing scheme. Comparison results with a standard algorithm in terms of robustness, speed and accuracy are provided. This face detection algorithm is incorporated into our previously developed rule-based AF method to achieve real-time face-priority AF on an actual digital camera platform.

I. Introduction

Nowadays, most digital and cell-phone cameras have an auto focus (AF) feature allowing users to capture focused images without any user intervention. Auto focusing involves the adjustment of the distance between the lens and the image sensor in order to obtain a focused image. Various passive AF techniques, e.g. [1], [2] have been developed to replace the tedious process of manual focusing by merely using the captured image information and not using any additional distance sensor. In passive AF, a measure of image sharpness or a sharpness function is extracted from captured images at different lens positions and then the in-focus position is obtained by locating the peak of the sharpness function.