Human Face Processing with 1.5D Models

Abstract. Integral projections reduce the size of input data by transforming 2D images into significantly simpler 1D signals, while retaining useful information to solve important computer vision problems like object detection, location, and tracking. However, previous attempts typically rely on simple heuristic analysis such as searching for minima or maxima in the resulting projections. We introduce a more rigorous and formal modeling framework based on a small set of integral projections –thus, we will call them 1.5D models – and show that this model-based analysis overcomes many of the difficulties and limitations of alternative projection methods. The proposed approach proves to be particularly adequate for the specific domain of human face processing. The problems of face detection, facial feature location, and tracking in video sequences are studied under the unifying proposed framework

Human Face Processing with 1.5D Models Anonymous

Abstract. Integral projections reduce the size of input data by transforming 2D images into significantly simpler 1D signals, while retaining useful information to solve important computer vision problems like object detection, location, and tracking. However, previous attempts typically rely on simple heuristic analysis such as searching for minima or maxima in the resulting projections. We introduce a more rigorous and formal modeling framework based on a small set of integral projections –thus, we will call them 1.5D models – and show that this model-based analysis overcomes many of the difficulties and limitations of alternative projection methods. The proposed approach proves to be particularly adequate for the specific domain of human face processing. The problems of face detection, facial feature location, and tracking in video sequences are studied under the unifying proposed framework. Key words: 1.5D object models, integral projections, face detection, facial feature location, face tracking.