Head Yaw Estimation From Asymmetry of Facial Appearance

Abstract

Abstract-This paper proposes a novel method to estimate the head yaw rotations based on the asymmetry of 2-D facial appearance. In traditional appearance-based pose estimation methods, features are typically extracted holistically by subspace analysis such as principal component analysis, linear discriminant analysis (LDA), etc., which are not designed to directly model the pose variations. In this paper, we argue and reveal that the asymmetry in the intensities of each row of the face image is closely relevant to the yaw rotation of the head and, at the same time, evidently insensitive to the identity of the input face. Specifically, to extract the asymmetry information, 1-D Gabor filters and Fourier transform are exploited. LDA is further applied to the asymmetry features to enhance the discrimination ability. By using the simple nearest centroid classifier, experimental results on two multipose databases show that the proposed features outperform other features. In particular, the generalization of the proposed asymmetry features is verified by the impressive performance when the training and the testing data sets are heterogeneous.Index Terms-Fourier transform, Gabor filters, head yaw estimation, linear discriminant analysis (LDA), nearest centroid (NC) classifier.

Automatic Calibration Method for Driver's Head Orientation in Natural Driving Environment

“…Learning techniques such as principal component analysis (PCA), kernel PCA (KPCA), linear discriminant analysis (LDA), and kernel discriminate analysis have been used to extract texture features, and these features are then classified to obtain the discrete head orientation [21]. Ma et al analyzed the asymmetry of the facial image by using a Fourier transform to estimate the driver’s continuous yaw [22]. The methods based on texture features are relatively reliable because specific facial features do not need to be localized.…”