Face Pose Estimation using a Tree of Boosted Classifiers

Face detection in images or video sequences is a very challenging problem. It has a wide range of applications but at the same time it presents a great number of difficulties, since faces are non-rigid and very changeable objects that can adopt a lot of different poses and with a high inter and intra-person variation and a high sensitivity to lighting conditions. Along this document, a new approach to the face detection and pose estimation problem is given. This approach is based on the method proposed by Viola and Jones in [1] but considering a wide range of face poses, varying the elevation and the out-of-plane rotation, and building specific classifiers for each one. The proposed method can be easily adapted to consider other poses or to detect other objects. Especially, this approach is interesting when an object that can adopt several positions want to be detected, since the partition of the pose space allows to build classifiers specialised in only one or a few poses, which limits the large variance of the global class, the class containing all the poses. In order to facilitate the reproduction of all the processes done in this document, we have used standard face datasets to train and test the system

Face Detection by Direct Convexity Estimation

. We suggest a novel attentional mechanism for detection of smooth convex and concave objects based on direct processing of intensity values. The operator detects the regions of the eyes and hair in a facial image, and thus allows us to infer the face location and scale. Our operator is robust to variations in illumination, scale, and face orientation. Invariance to a large family of functions, serving for lighting improvement in images, is proved. An extensive comparison with edgebased methods is delineated. 1 Introduction Edge detection was, so far, the core of most state of the art techniques for attentional mechanisms as well as face detection (see [4], [6]). This excludes some recent works which utilize neural networks ([9],[7]), color histograms ([2],[8]), or shape statistics ([1],[5]) for face detection. Though one cannot disregard their advantages, edge maps sustain severe flaws such as: sensitivity to changes in illumination, strong effect of surrounding objects, and inabilit..

Abstract Face Detection by Direct Convexity Estimation

We suggest a novel attentional mechanism for detection of smooth convex and concave objects based on direct processing of intensity values. The operator detects the regions of the eyes and hair in a facial image, and thus allows us to infer the face location and scale. Our operator is robust to variations in illumination, scale, and face orientation. Invariance to a large family of functions, serving for lighting improvement in images, is proved. An extensive comparison with edge-based methods is delineated. Key words: Face Detection. Convexity. Gradient Argument.