Accurate ground-truth pose is essential to the training of most existing head pose estimation methods. However, in many cases, the "ground truth" pose is obtained in rather subjective ways, such as asking the subjects to stare at different markers on the wall. Thus it is better to use soft labels rather than explicit hard labels to indicate the pose of a face image. This paper proposes to associate a multivariate label distribution (MLD) to each image. An MLD covers a neighborhood around the original pose. Labeling the images with MLD can not only alleviate the problem of inaccurate pose labels, but also boost the training examples associated to each pose without actually increasing the total amount of training examples. Four algorithms are proposed to learn from MLD. Furthermore, an extension of MLD with the hierarchical structure is proposed to deal with fine-grained head pose estimation, which is named hierarchical multivariate label distribution (HMLD). Experimental results show that the MLD-based methods perform significantly better than the compared state-of-the-art head pose estimation algorithms. Moreover, the MLD-based methods appear much more robust against the label noise in the training set than the compared baseline methods.

Lack of sufficient training data with exact ages is still a challenge for facial age estimation. To deal with such problem, a method called Label Distribution Learning (LDL) was proposed to utilize the neighboring ages while learning a particular age. Later, an adaptive version of LDL called ALDL was proposed to generate a proper label distribution for each age. However, the adaptation process requires more training data, which creates a dilemma between the performance of ALDL and the training data. In this paper, we propose an algorithm called Semi-supervised Adaptive Label Distribution Learning (SALDL) to solve the dilemma and improve the performance using unlabeled data for facial age estimation. On the one hand, the utilization of unlabeled data helps to improve the adaptation process. On the other hand, the adapted label distributions conversely reinforce the semi-supervised process. As a result, they can promote each other to get better performance. Experimental results show that SALDL performs remarkably better than state-of-the-art algorithms when there are only limited accurately labeled data available.

Zero-shot learning predicts new class even if no training data is available for that class. The solution to conventional zero-shot learning usually depends on side information such as attribute or text corpora. But these side information is not easy to obtain or use. Fortunately in many classification tasks, the class labels are ordered, and therefore closely related to each other. This paper deals with zero-shot learning for ordinal classification. The key idea is using label relevance to expand supervision information from seen labels to unseen labels. The proposed method SIDL generates a supervision intensity distribution (SID) that contains each label's supervision intensity, and then learns a mapping from instance to SID. Experiments on two typical ordinal classification problems, i.e., head pose estimation and age estimation, show that SIDL performs significantly better than the compared regression methods. Furthermore, SIDL appears much more robust against the increase of unseen labels than other compared baselines.