Head pose estimation is a challenging topic in computer vision with a large area of applications. There are a lot of methods which have been presented in the literature to undertake pose estimation so far. Even though the efficiency of these methods is acceptable, the sensitivity to external conditions is still being a big challenge. In this paper, we come up with a new model to overcome the problem of head poses estimation. First, the face images are converted into one-dimensional vectors as a time series using the Peano–Hilbert space-filling curve. Then, we convert these numerical series into symbolic sequences with adequate dimensionality reduction approaches. These sequences are then used as input of an encode–decoder neural network to learn and generate labels of the faces orientations. We have evaluated our model on several databases, and the experimental results have shown that the proposed method is very competitive compared to other well-known approaches.

中文翻译：

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利用符号序列的深度学习进行可靠的头部姿势估计

头部姿势估计是计算机视觉中具有很大应用领域的一个具有挑战性的主题。迄今为止，文献中已经提出了许多方法来进行姿势估计。尽管这些方法的效率是可以接受的，但对外部条件的敏感性仍然是一个很大的挑战。在本文中，我们提出了一个新的模型来克服头部姿势估计的问题。首先，使用Peano-Hilbert空间填充曲线将面部图像转换为一维矢量作为时间序列。然后，我们使用适当的降维方法将这些数字序列转换为符号序列。然后将这些序列用作编码解码器神经网络的输入，以学习并生成人脸方向的标签。我们已经在几个数据库上评估了我们的模型，