Footprint-based personal recognition

Privacy-Constrained Biometric System for Non-Cooperative Users

With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.

Real-Time User Identification and Behavior Prediction Based on Foot-Pad Recognition

In the IoT (Internet of things)-based smart home, the technology for recognizing individual users among family members is very important. Although research in areas such as image recognition, biometrics, and individual wireless devices is very active, these systems suffer from various problems such as the need to follow an intentional procedure or own a specific device. Furthermore, with a centralized server system for IoT service, it is difficult to guarantee real-time determinism with high accuracy. To overcome these problems, we suggest a method of recognizing users in real time from the foot pressure characteristics measured as a user steps on a footpad. The proposed model in this paper uses a preprocessing algorithm to determine and generalize the angle of foot pressure. Based on this generalized foot pressure angle, we extract nine features that can distinguish individual human beings, and employ these features in user-recognition algorithms. Performance evaluation of the model was conducted by combining two preprocessing algorithms used to generalize the angle with four user-recognition algorithms.

Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals

Everyone's walking style is unique, and it has been shown that both humans and computers are very good at recognizing known gait patterns. It is therefore unsurprising that dynamic foot pressure patterns, which indirectly reflect the accelerations of all body parts, are also unique, and that previous studies have achieved moderate-to-high classification rates (CRs) using foot pressure variables. However, these studies are limited by small sample sizes (n < 30), moderate CRs (CR ≃ 90%), or both. Here we show, using relatively simple image processing and feature extraction, that dynamic foot pressures can be used to identify n = 104 subjects with a CR of 99.6 per cent. Our key innovation was improved and automated spatial alignment which, by itself, improved CR to over 98 per cent, a finding that pointedly emphasizes inter-subject pressure pattern uniqueness. We also found that automated dimensionality reduction invariably improved CRs. As dynamic pressure data are immediately usable, with little or no pre-processing required, and as they may be collected discreetly during uninterrupted gait using in-floor systems, foot pressure-based identification appears to have wide potential for both the security and health industries.

User identification using gait patterns on UbiFloorII

This paper presents a system of identifying individuals by their gait patterns. We take into account various distinguishable features that can be extracted from a user's gait and then divide them into two classes: walking pattern and stepping pattern. The conditions we assume are that our target environments are domestic areas, the number of users is smaller than 10, and all users ambulate with bare feet considering the everyday lifestyle of the Korean home. Under these conditions, we have developed a system that identifies individuals' gait patterns using our biometric sensor, UbiFloorII. We have created UbiFloorII to collect walking samples and created software modules to extract the user's gait pattern. To identify the users based on the gait patterns extracted from walking samples over UbiFloorII, we have deployed multilayer perceptron network, a feedforward artificial neural network model. The results show that both walking pattern and stepping pattern extracted from users' gait over the UbiFloorII are distinguishable enough to identify the users and that fusing two classifiers at the matching score level improves the recognition accuracy. Therefore, our proposed system may provide unobtrusive and automatic user identification methods in ubiquitous computing environments, particularly in domestic areas.

Individualizing characteristics of footprints in Gujjars of North India—Forensic aspects

Careful examination of foot impressions can provide important evidences and clues in a crime scene investigation. The present study is conducted on a cross-sectional sample of 1040 adult male Gujjars inhabiting the sub-Himalayan region of North India. The study describes the utility of individualizing characteristics of footprints in forensic examinations. Various features of the toes, humps in the toe line, phalange marks, flatfoot condition, pits, cracks, corns, etc., were studied. Frequency of some of these characters has also been recorded. The frequency of the tibialis-type foot is the highest, followed by fibularis-type, then intermediate-type and midularis-type is found to be least frequent among the sample. Three humps have been found most often in footprints, followed by two humps, four humps, and then five humps and one hump are found to be least frequent. Flatfoot condition is found to be present in 1.54% of the sample population and the trait also shows bilateral variation. Phalange marks, crease marks, pits, deformity, etc., are also demonstrated with suitable examples in the present population. These characteristic features can provide useful clues to establish personal identity whenever complete or partial footprints are recovered at the crime scene and can help in including or excluding the possible presence of individual at the scene of crime.

Personal recognition rate improvement using head-top image and an application to walking subject

A personal identification method using head-top image is improved for the smart house. The personal recognition rate was 86.4 percent in the previous report from eleven subjects who stood still. It was improved to 100 percent in this study by using the same image database of the previous report. Moreover, head-top image sequences were acquired when the subjects under the thermo camera started walking, and the personal recognition rate was 52 percent from six subjects.

Personal recognition using head-top image for health-monitoring system in the home

Automatic health-monitoring systems for the smart house are being developed for the elderly. An automatic health-monitoring system needs a way of personal recognition when two or more aged persons live together. We propose a personal recognition method based on the space spectrum of the head-top image. We examined 33 head-top images from eleven subjects and achieved a personal recognition rate of 86.4 percent. When one subject with thinning hair was excluded, the personal recognition rate was 90.0 percent in 30 head-top images from ten subjects.