Traditional image-based face detection methods use a window based scanning technique where the window is scanned pixel-by-pixel to search for faces in various positions and scales within an image. Therefore, they require high computation cost and are not adequate to the real time applications. In this paper, we introduce a novel coarse-to-fine classification method for image-based face detection using multiple face classifiers. A coarse location of a face is first classified by the gradient feature based face classifier where the window is scanned in large moving steps. From the coarse location of a face, the fine classification is performed to identify the local image' as a face using the multiple face classifiers where the window is finely scanned. The multiple face classifiers are designed to take gradient, texture and pixel intensity features and trained by back propagation learning algorithm. Experimental results demonstrate that our proposed method can reduce up to 90.4% of the number of scans compared to the exhaustive full scanning technique and provides the high detection rate.