Pixel encoding for unconstrained face detection

Abstract

In an uncontrolled environment, many of the face detection algorithms lack robustness due to their design. The present research on unconstrained face detection is focused on handcrafted and visual features in isolation. We propose a novel approach to use handcrafted as well as visual features together for improvement in face detection to achieve robustness. The algorithm uses a side-view face detector, which divides the problem space into two: side view face detection and frontal face detection. For frontal faces, Discrete Wavelet Transform (DWT) followed by the encoding of Eyes like landmarks (ELL) pixels is proposed in this work. A Human trait that helps to make decisions even better when they are taken together with the help of more than one decision makers is modeled in this work. To achieve this, a combination of handcrafted and visual features is used. Further to improve classification and to provide a better decision, a faster second stage classification scheme is introduced. The result shows an improvement when handcrafted and visual features are combined instead of using them separately.