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Pedestrian identification system often uses false acceptance rate (FAR) and false rejection rate (FRR) as performance evaluation criteria. 

Considering the classifier factor, we only compare the results of different feature representation methods, hence we choose to use multi-class support vector machine (SVM) as our classifier. And the regularization coefficient is set as 0.1 for different feature representation methods. In sparse coding framework, we used only Dense SIFT [4] descriptor as our low-level descriptor, throughout the experiment. In out setup, the SIFT descriptor are extracted from patches densely located by every 4 pixels on the image, under the scale, 4x4 respectively. The dimension of each SIFT descriptor is 128. During feature representation learning process, PCA is our benchmark considering the similar method described in [1,11]. We set reduced dimensionality as 200. Restricted Boltzmann Machine [5] is a popular deep learning feature selection method, which is proved to be effectiveness in digital recognition. Still, we set reduced dimensionality as 200. We choose to compare our works with it since it is a related representative of unsupervised learning method. DHSC is our proposed method. Where dictionary size is set as 128 and regularization penalty term is set as 0.15. 

Table. 1, 2, 3 show the experimental results of comparison between different methods. Generally speaking, from the results, we can see the effectiveness of sparsity in representing the structure information. Discriminative hierarchical linear-constraint sparse coding method outperforms the PCA+LDA and RBM+LDA methods, since the sparsity penalty terms in (5) ensure the sparsity of representation while preserve the important structure information from cumulative foot pressure images. 

However, the shoes-invariant cumulative foot pressure image recognition is still a difficult problem. All three representative methods do not well handle the problem that recognizing shoes-wearing subject when registered one is barefoot one. As fig. 3 illustrate, the appearance of barefoot 

cumulative foot pressure images are different from that of shoes wearing one. Even for human, we may not distinguish these types of data easily. All three methods and other possible traditional computer vision methods are all focus on modeling the appearance information of image. Future works need to take into account of other factor and other correlated modal like gait pose images together. 

Fig. 4 illustrate the important components of a given 

cumulative foot pressure image. These components are the key of recognizing the corresponding pedestrian and distinguish it with the cumulative foot pressure images of others. Table 1. Experiments are conducted under the condition that bare feet are used in registering and testing. 

 PCA [1,11]+LDA RBM [5]+LDA DHSC 

FRR 0.2% 0.2% **0.1%** FAR 0.6% 1.0% **0.9%** Table 2. Experiments are conducted under the condition that bare feet are used in registering while shoes wearing ones are used in testing. 

 PCA[1,11]+LDA RBM[5]+LDA DHSC 

FRR 28.6% 22.0% **19.0%** 

FAR 1.3% 1.0% 1.0% 

Table 3. Experiments are conducted under the condition that wearing shoes feet are used in registering while shoes wearing ones are used in testing. 

 PCA [1,11]+LDA RBM [5]+LDA DHSC 

FRR 0.5% 0.4% **0.2%** FAR 2.0% 1.0% **0.02%** 

## 6. **Conclusion And Future Works**

In this paper, we discuss the potential of cumulative foot pressure image as a biometric and present a possible solution to its challenge in shoes-invariant recognition. However, the results prove the effectiveness of the proposed method. Discriminative hierarchical linear-constraint sparse coding method achieves best performance comparing with other potential methods due to that it model both the structure and appearance information of a given cumulative foot pressure image. Besides, it sounds reasonable that the key components 