Methods using salient facial patches (SFPs) play a significant role in research on facial expression recognition. However, most SFP methods use only frontal face images or videos for recognition, and they do not consider head position variations. We contend that SFP can be an effective approach for recognizing facial expressions under different head rotations. Accordingly, we propose an algorithm, called profile salient facial patches (PSFP), to achieve this objective. First, to detect facial landmarks and estimate head poses from profile face images, a tree-structured part model is used for pose-free landmark localization. Second, to obtain the salient facial patches from profile face images, the facial patches are selected using the detected facial landmarks while avoiding their overlap or the transcending of the actual face range. To analyze the PSFP recognition performance, three classical approaches for local feature extraction, specifically the histogram of oriented gradients (HOG), local binary pattern, and Gabor, were applied to extract profile facial expression features. Experimental results on the Radboud Faces Database show that PSFP with HOG features can achieve higher accuracies under most head rotations.

使用显着面部贴片（SFP）的方法在面部表情识别研究中起着重要作用。但是，大多数SFP方法仅使用正面人脸图像或视频进行识别，并且不考虑头部位置的变化。我们认为，SFP可以是识别不同头部旋转情况下面部表情的有效方法。因此，我们提出了一种称为轮廓显着面部补丁（PSFP）的算法来实现此目标。首先，为了检测脸部界标并根据轮廓脸部图像估计头部姿态，将树状结构的零件模型用于无姿态界标定位。其次，为了从轮廓面部图像获得显着的面部补丁，使用检测到的面部标志来选择面部补丁，同时避免它们的重叠或超出实际面部范围。为了分析PSFP识别性能，采用了三种经典的局部特征提取方法，特别是定向梯度直方图（HOG），局部二进制模式和Gabor，来提取轮廓面部表情特征。Radboud Faces数据库上的实验结果表明，具有HOG功能的PSFP在大多数头部旋转下都可以实现更高的精度。