Estimating Head Orientation Using a Combination of Multiple Cues

Abstract

SUMMARYThis paper proposes an appearance-based novel descriptor for estimating head orientation. Our descriptor is inspired by the Weberbased feature, which has been successfully implemented for robust texture analysis, and the gradient which performs well for shape analysis. To further enhance the orientation differences, we combine them with an analysis of the intensity deviation. The position of a pixel and its intrinsic intensity are also considered. All features are then composed as a feature vector of a pixel. The information carried by each pixel is combined using a covariance matrix to alleviate the influence caused by rotations and illumination. As the result, our descriptor is compact and works at high speed. We also apply a weighting scheme, called Block Importance Feature using Genetic Algorithm (BIF-GA), to improve the performance of our descriptor by selecting and accentuating the important blocks. Experiments on three head pose databases demonstrate that the proposed method outperforms the current state-of-the-art methods. Also, we can extend the proposed method by combining it with a head detection and tracking system to enable it to estimate human head orientation in real applications.

A New Feature Extraction Algorithm to Extract Differentiate Information and Improve KNN-based Model Accuracy on Aquaculture Dataset

“…Thus, we can prune the pixel data under the main diagonal to prevent redundancy and speed up computation time in the next process. This step is called pixel pruning and the total number of pixel data after pruning is always satisfying equation 8 [12].…”