The study of human action recognition is an area with important academic and application values. It is widely applied to the fields of intelligent surveillance

video retrieval

human interaction

live entertainment

virtual reality

and health care. In human learning

a teacher can provide students with information hidden in examples

explanations

comments

and comparisons. However

the information offered by a teacher is seldom applied to the field of human action recognition. This study considers 3D depth features as privileged information to help solve human action recognition problems and to demonstrate the superiority of a new learning paradigm over the classical learning paradigm. This paper reports on the details of the new paradigm and its corresponding algorithms. The human body can be represented as an articulated system with rigid segments connected by joints. Human motion can be regarded as a continuous evolution of the spatial configuration of these rigid segments. With the recent release of depth cameras

an increasing number of studies have extracted the 3D positions of tracked joints to represent human activities， these studies have achieved relatively good performance. However

relative 3D algorithms have numerous application limits resulting from inconvenient equipment and costly computation. The extraction of joints from RGB video sequences is difficult

which limits recognition result. This study applies 3D depth features as privileged information to solve the aforementioned challenge. In particular

we apply a new skeletal representation that explicitly models the 3D geometric relationships among different body parts that use rotations and translations in 3D space in the lie group. We use different algorithms

including motion scale-invariant feature transform

motion boundary histograms

and different combined descriptors

for the basic 2D features to unite privileged information. Privileged information is available in the training stage

but not in the testing stage. Similar to the traditional classification problem

the new algorithm focuses on learning a new classifier

i.e.

support vector machine+ (SVM+). The SVM+ algorithm

which considers both privileged and unprivileged information

is highly similar to SVM algorithms in terms of determining solutions in the classical pattern recognition framework. In particular

it finds the optimal separating hyperplane

which incurs a few training errors and exhibits a large margin. However

the SVM+ algorithm is computationally costlier than SVM. This study applies the new algorithm to the field of human activity recognition to provide convenience to the testing set because 3D information is only required in the training set. We evaluate our method in two challenge databases

namely

UTKinect-Action and Florence3D-Action

with three different 2D features. The SVM+ algorithm considers both 2D basic features and 3D privileged information

whereas SVM only uses 2D basic features. Results show that our proposed SVM+ outperforms SVM. Moreover

SVM+ is less sensitive to relevant parameters than SVM. This paper reports on the details of the recognition performance

varying numbers of training samples

different parameters

and confusion matrix for both SVM and SVM+ on the two datasets. The privileged information can help to reduce the noise of the original 2D basic features and increase the robustness of human activity recognition. The role of a teacher in providing remarks

explanations

and analogies is highly important. This study proposes a new human action recognition method based on privileged information. The experimental results of the two datasets show the effectiveness of our method in human action recognition. The proposed method is only required to extract 3D privileged information during the training process. A depth information acquisition device is not required during the testing process. This method exhibits high learning speed and low computational complexity. It can be extensively used in low-cost

real-time human action recognition.