# Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
## 5 Discussion  
The aim of this study was to highlight the potential of COP analysis for biometric purposes. I investigated whether the COP trajectory could discriminate among 36 individuals. The learning of 1350 strides per participant under supervision using deep CNNs enabled the classification of 150 previously unseen strides with an overall accuracy of 99.9%. In verification scenarios, the best CNN was able to discriminate gaits of authorized users with an EER of 0.29%. Transfer learning results  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Authorized** & **Unauthorized** & **AUC** & **AUC (1 st** & **AUC (3 rd** & **EER** & **EER (1 st** & **EER (3 rd** \\
**Users** & **Users** & **(Median)** & **Quartile)** & **Quartile)** & **(Median)** & **Quartile)** \\ \hline
10 & 26 & 0.99997 & 0.99994 & 0.99999 & 0.27\% & 0.23\% & 0.31\% \\
15 & 21 & 0.99997 & 0.99994 & 0.99998 & 0.25\% & 0.19\% & 0.33\% \\
20 & 16 & 0.99995 & 0.99991 & 0.99996 & 0.33\% & 0.25\% & 0.41\% \\
25 &showed that pre-trained CNNs can successfully learn gaits of previously unseen individuals when fed with only two to four strides. With only two to three misclassified items over 2700 attempts, CNNs were found to perform very well in the task of recognizing individuals through their COP trace on the treadmill belt (Table 2). In addition, in verification scenarios, authorized users can be identified with a high accuracy (EER 0.29%, Table 3). Table 4 compares these results with those of previous representative footstep recognition studies. Classification of COP traces via CNN seems to perform equally or better than other approaches. However, the inhomogeneity of the number of investigated footsteps and individuals among studies makes it difficult to reach a conclusion. Note that previous studies relied on complex procedures of feature engineering and data reduction before classification. In contrast, here, CNNs were directly fed with raw COP signals. It is also worth noting that the previous studies that reported the highest accuracy [16; 23] analyzed unshod walking and therefore have limited applicability in real-life situations. The outside-the-lab application of the 3D GRF method [14; 15] is also questionable, given that it uses complex and expensive force platforms that can cover only a very limited surface. These promising results are tempered by the fact that they were obtained from a small sample of individuals. It is uncertain how unique COP shapes (Figures 1, 2 and 6) are at the population level. From a global point of view, gait is an idiosyncratic feature that is determined by an individual's characteristic motion conditioned in part by unique anatomical traits such as body weight, limb lengths, joint morphologies, and foot shapes. Many studies have evidenced that an individual's gait is sufficiently unique to be used for biometric recognition [2]. For example, it has been demonstrated that the variability of limb lengths in the population and their unique combinations in each person make possible a correct identification in large samples [54]. Similarly, it can be assumed that an individual's typical COP trajectory (Figures 2 and 6) also results from these unique combinations of anatomical traits and characteristic motions. The hypothesis of a COP shape's uniqueness is supported by the transfer learning results; increasing the number of individuals to discriminate by 20% (from 30 to 36) does not lower the recognition rate (Table 2). The high performance of CNNs was obtained with only a very slight regularization, with no dropout. Interestingly, the novel Swish algorithm revealed itself as the best activation method, in line