Extraction of discriminative features from salient facial patches plays a
vital role in effective facial expression recognition. The accurate detection
of facial landmarks improves the localization of the salient patches on face
images. This paper proposes a novel framework for expression recognition by
using appearance features of selected facial patches. A few prominent facial
patches, depending on the position of facial landmarks, are extracted which are
active during emotion elicitation. These active patches are further processed
to obtain the salient patches which contain discriminative features for
classification of each pair of expressions, thereby selecting different facial
patches as salient for different pair of expression classes. One-against-one
classification method is adopted using these features. In addition, an
automated learning-free facial landmark detection technique has been proposed,
which achieves similar performances as that of other state-of-art landmark
detection methods, yet requires significantly less execution time. The proposed
method is found to perform well consistently in different resolutions, hence,
providing a solution for expression recognition in low resolution images.
Experiments on CK+ and JAFFE facial expression databases show the effectiveness
of the proposed system.

本文提出了一种用于面部表情识别的新框架，通过使用选择面部补丁的外观特征，使用一对一分类方法来分类每对表情类，提取包含区分特征的显著补丁，并针对不同表情类选择不同的显著补丁，提出了一个自动学习 - 无费用的面部标记检测技术，该技术在不同分辨率下表现稳定，在 CK + 和 JAFFE 面部表情数据库上展示了该系统的有效性。