# Human Skeleton Data Augmentation for Person Identification over Deep Neural Network
## 2 Related Work  
The idea of using anthropometric features in person identification was first proposed by [PERSON] et al. [22]. The authors calculated the length of 11 human body parts captured by the Kinect sensor. The parts used were as follows: (1) left and right upper arms, (2) left and right forearms, (3) left and right sharks, (4) left and right thighs, (5) thoracic spine, (6) cervical spine, and (7) height. Using the average length of each body part, the authors tested four learning algorithms, including MLP and \(k\)-NN, on their own data consisting of eight subjects. According to their results, \(k\)-NN outperformed the other algorithms, achieving about 99.6% accuracy. [PERSON] et al. [23] investigated the usefulness of the anthropometric features on large-scale datasets. After capturing skeleton data from 164 individuals using the Kinect sensor, they tested the two learning algorithms, MLP and \(k\)-NN, on their own data. The results showed that as the number of subjects for identification increased, the accuracy of both the algorithms decreased. From the evaluation with different numbers of subjects, it was shown that \(k\)-NN always outperformed MLP. [PERSON] et al. [24] extended their previous work by exploring the effect of different numbers of subjects on the person identification accuracy. In addition, to improve the accuracy, they proposed to use the anthropometric features for all limbs rather than for only a limited number of limbs. As a result, 20 anthropometric features, including average lengths of the left and right shoulders, hands, hips, and feet, were used for person identification. Using these features, the authors tested three learning algorithms: SVM, \(k\)-NN, and MLP. As the number of subjects for identification increased by 15, in the range from 5 to 140, the accuracy of all the algorithms decreased. The \(k\)-NN algorithm outperformed the other algorithms except when the number of subjects was less than 35. By utilizing the extended anthropometric features, the accuracy of \(k\)-NN increased from approximately 80% [23] to approximately 85.4% [24]. The same authors analyzed their previous results in a subsequent study [25]. Focusing on \(k\)-NN, they explored the importance of each anthropometric feature to identify individuals. By removing each anthropometric feature without repetition of the measurement, the authors measured the average accuracy of \(k\)-NN using the remaining features. In addition, the importance of the corresponding feature was assessed by calculating the decrease in the average accuracy. From the results, the importance of the anthropometric features in person identification was seen to differ for different features. According to their results, the average length of the right foot was the most important anthropometric feature utilized to identify individuals. Motivated by these results, [PERSON] et al. [26] proposed to apply the majority voting method to the person identification algorithm. In their studies, a person was identified by the voting results of 100 \(k\)-NN classifiers. For the training of each \(k\)-NN classifier, 10 randomly selected anthropometric features from a total of 20 were used. The authors tested their proposed method on the public person identification dataset proposed by [PERSON] et al. [25]. The average accuracy could be improved by 2.2% from 83.9% to 86.1% using their voting-based method. [PERSON] et al. [27] created a connection between 2D silhouette- and 3D skeleton-based person identification methods. Using the Kinect sensor, they built a person identification dataset composed of 2D silhouette and 3D skeleton sequences captured from 52 subjects. In their study, they extracted eight anthropometric features from the 3D skeleton sequences and then calculated a score defined by the weighted sum of the values of features. To determine the weight of each feature, they measured the correct classification rate when the corresponding feature was used for person identification.