A Survey of Behavioral Biometric Gait Recognition: Current Success and Future Perspectives

Singh, Jasvinder Pal; Jain, Sanjeev; Arora, Sakshi; Singh, Uday Pratap

doi:10.1007/s11831-019-09375-3

Cited by 41 publications

(25 citation statements)

References 204 publications

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“…It has also been used in human psychology where gait analysis using point lights employed for recognition of emotional patterns. The same idea was extended and ultimately resulted in the development of gait signatures through which the identification of individuals can be performed [1]. Borrowing from this, computer vision-based approaches have also used motion analysis and human movement modeling for person identification [2].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

et al. 2021

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Section: Introductionmentioning

confidence: 99%

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

et al. 2021

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“…Although passwords have been used regularly for authenticating users for years, they are losing their popularity as passwords can be cracked or stolen quite easily. Biometric security was introduced as a better solution to verify individuals based on their unique characteristics [1]. Physical biometrics, such as fingerprints, face recognition and iris scanning, are currently being used extensively in many applications to secure access to servers and services.…”

Section: Introductionmentioning

confidence: 99%

Behavioral Biometrics: Past, Present and Future

Sharma¹,

Elmiligi²

2022

Recent Advances in Biometrics

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Behavioral biometrics are changing the way users are authenticated to access resources by adding an extra layer of security seamlessly. Behavioral biometric authentication identifies users based on a set of unique behaviors that can be observed when users perform daily activities or interact with smart devices. There are different types of behavioral biometrics that can be used to create unique profiles of users. For example, skill-based behavioral biometrics are common biometrics that is based on the instinctive, unique and stable muscle actions taken by the user. Other types include style-based behavioral biometrics, knowledge-based behavioral biometrics, strategy-based behavioral biometrics, etc. Behavioral biometrics can also be classified based on their use model. Behavioral biometrics can be used for one-time authentication or continuous authentication. One-time authentication occurs only once when a user requests access to a resource. Continuous authentication is a method of confirming the user’s identity in real-time while they are using the service. This chapter discusses the different types of behavioral biometrics and explores the various classifications of behavioral biometrics-based on their use models. The chapter highlights the most trending research directions in behavioral biometrics authentication and presents examples of current commercial solutions that are based on behavioral biometrics.

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“…Shanahan et al ( 2017 ) shows that the BCI system performs better in terms of accuracy and control for older people than children due to active participation and repetition of specific tasks leading to higher signal quality. It is worth mentioning that there are other wearable and non-wearable technologies used for gait and balance assessment (Shanahan et al, 2017 ; Singh et al, 2019 ), which do not involve brain signals. Examples of those wearable technologies include optical motion capture systems, instrumented walkway, and force platforms.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Khan

Naseer

Yazidi

et al. 2021

Front. Hum. Neurosci.

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Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

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A Survey of Behavioral Biometric Gait Recognition: Current Success and Future Perspectives

Cited by 41 publications

References 204 publications

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

Behavioral Biometrics: Past, Present and Future

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

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