# Translation-Invariant Representation For Cumulative Foot Pressure Images

Shuai ZHENG†, Kaiqi HUANG†, and Tieniu TAN†
†National Laboratory of Pattern Recognition, Chinese Academy of Science No.95 Zhongguancun East Road, Haidian District, Beijing, China E-mail : †{szheng, kqhuang, tnt}@nlpr.ia.ac.cn Abstract Human can be distinguished by different limb movements and unique ground reaction force. Cumulative foot pressure image is a 2-D cumulative ground reaction force during one gait cycle. Although it contains pressure spatial distribution information and pressure temporal distribution information, it suffers from several problems including different shoes and noise, when putting it into practice as a new biometric for pedestrian identification. In this paper, we propose a hierarchical translation-invariant representation for cumulative foot pressure images, inspired by the success of Convolutional deep belief network for digital classification. Key contribution in our approach is discriminative hierarchical sparse coding scheme which helps to learn useful discriminative high-level visual features. Based on the feature representation of cumulative foot pressure images, we develop a pedestrian recognition system which is invariant to three different shoes and slight local shape change. Experiments are conducted on a proposed open dataset that contains more than 2800 cumulative foot pressure images from 118 subjects. Evaluations suggest the effectiveness of the proposed method and the potential of cumulative foot pressure images as a biometric. Key words Identification, Ground Reaction Force, Feature Representation, Biometric, Footprint, and Gait 

## 1. **Introduction**

Walking can be considered as an inherent attribute of a person: each person has his/her own unique limb movement pattern and ground reaction force during walking [11]. This attribute allows one to identify a person. Previous works [19, 20] have developed person identification system using the unique limb movement pattern, which is called gait. However, recent gait recognition systems do not work well since there are many challenges in practice, such as illumination change in environment, clothing variation for the same individual and different camera viewpoint for probe and gallery data. Previous biomedical, robotic and forensic studies [8, 12, 14] have proposed two kinds of pedestrian recognition system based on ground reaction force information. One is utilizing barefoot print image, another is based on 1-D ground reaction force signal. Although the uniqueness of barefoot has been proved [12] , but shoes-off recognition process is not suitable for most scenarios [2]. Besides, 1-D ground reaction force signal [14] 
does not provide enough information to recognize individual, although it seems practical to help robot to distinguish pedestrian and other objects. In this paper, we propose a pedestrian recognition system using cumulative foot pressure images. Cumulative foot pressure image contains cumulative spatial and temporal force information during one gait cycle [19, 18], which may help it handling the difficulties in recognizing different shoes-wearing individual. 

For some security scenarios such as jailhouse security system, bathhouse, entrance of public transportation, and entrance of Japanese house, camera-based recognition system does not work well since it suffers from various problems such as privacy, difficult extreme environment and crowd backgrounds. The proposed recognition system using cumulative foot pressure images can be deployed in these scenarios, taking place of camera-based system. Different from gait, cumulative foot pressure image is acquired by floor pressure sensing device [14] rather than camera. Hence there is no light illumination change or different camera viewpoint 