

![0_image_0.png](0_image_0.png)

it. Besides, cumulative foot pressure images can help to identify the same individual with different clothes since it doesn't rely on the appearance of individual. Furthermore, it may be more suitable for those specific scenarios. What's more important is that it does not have privacy problem since it does not need to take extra information such as appearance of individual. 

In this paper, we propose recognition task based on cumulative foot pressure images: verification (same or not, given two cumulative foot pressure image set). Fig. 1 illustrates the overview process of the proposed pedestrian recognition system using cumulative foot pressure images. When a person walks through foot pressure measurement floor, cumulative foot pressure images are acquired. Based on the acquired cumulative foot pressure image set, there are two major components to achieve the proposed two recognition tasks: cumulative foot pressure image representation and classification. The extracted descriptor is required to be not only discriminative but also invariant to apparent changes due to different shoes and noise. Based on the representation, the classification should be robust to shape variations. As shown in Fig. 1, these requirements render cumulative foot pressure image recognition a challenging problem. 

Currently, descriptor-based approaches [3, 4, 6, 7] are proved to be effectiveness in many representative challenge recognition dataset such as MINIST [6], and Caltech101 [6]. Many low-level descriptors such as SIFT or HOG are designed to be invariant to minor translations of input images. However, these low-level descriptors are not practical for the proposed system since they lose a lot of structure information (in terms of object part) which is crucial for shoes-invariant cumulative foot pressure image recognition. Several authors have proposed unsupervised methods to learn image descriptors based on sparse/overcomplete decomposition [10], but they have not been proved to be local invariance. Many successful object recognition proposals have appeared for unsupervised learning of locally-invariant descriptors, which also use sparsity criteria [21]. Besides, the sparse coding and locality-constrained sparse coding have been proved to be efficient for more challenge PASCAL VOC object recognition dataset [22]. Hence sparsity and locality criteria have the potential in our proposed system. 

Our goal is to learn a feature representation model for the cumulative foot pressure image which preserves discriminative local and global structure information. Hence, we propose a discriminative hierarchical locality-constrained sparse coding scheme for representing cumulative foot pressure image. The whole representation scheme is consist of two steps, one is dictionary learning, and another is hierarchical coding. 

The remainder of this paper is organized as follows. In Section 2, an overview description of feature representation for cumulative foot pressure image is given. In Section 3, sparse coding algorithm and its variation algorithms are described. In Section 4, a hierarchical locality-constrained sparse coding for cumulative foot pressure image is presented. In Section 5, experimental results on the dataset contains barefoot cumulative foot pressure image and shoes-wearing cumulative foot pressure image are reported, and corresponding analysis is presented. Conclusion and future works are given in Section 6. 

## 2. **Overview Of Pedestrian Recognition Using** Cumulative Foot Pressure Image

As Fig. 2 illustrate, similar to [22], we propose a hierarchical locality-sparse coding for cumulative foot pressure image. The differences between [17] and this paper are hierarchical coding strategies and pooling operations. 

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