A Novel Multimodal Biometrics System with Fingerprint and Gait Recognition Traits Using Contourlet Derivative Weighted Rank Fusion

Author(s):  
R. Vinothkanna ◽  
P. K. Sasikumar

This chapter presents the original idea of using social networks and context information in multimodal biometric for increased system security. A recently investigated study’s outcomes is presented, which showcase this idea as a new step in multi-biometric research. Since this method does not degrade the performance of the system and is not computationally expensive, it can be used in any biometric framework. However, as the amount of improvement depends on how distinctive and predictable people are in terms of their behavioral patterns, the method is most suitable for the predictable environments with some predefined behavioral routines. Fine tuning the system for each environment to find the most suitable parameters based on the behavioral patterns of that specific environment can result in better performance. This research is validated on example of gait recognition.


2011 ◽  
Vol 11 ◽  
pp. 503-519 ◽  
Author(s):  
A. Drosou ◽  
D. Ioannidis ◽  
K. Moustakas ◽  
D. Tzovaras

Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.


2004 ◽  
Author(s):  
Zongyi Liu ◽  
Laura Malave ◽  
Adebola Osuntogun ◽  
Preksha Sudhakar ◽  
Sudeep Sarkar
Keyword(s):  

2020 ◽  
Vol 11 (2) ◽  
pp. 1-33
Author(s):  
Haibing Lu ◽  
Xi Chen ◽  
Junmin Shi ◽  
Jaideep Vaidya ◽  
Vijayalakshmi Atluri ◽  
...  

2020 ◽  
pp. 1-12
Author(s):  
Linuo Wang

The current technology related to athlete gait recognition has shortcomings such as complicated equipment and high cost, and there are also certain problems in recognition accuracy and recognition efficiency. In order to improve the efficiency of athletes’ gait recognition, this paper studies the different recognition technologies of athletes based on machine learning and spectral feature technology and applies computer vision technology to sports. Moreover, according to the calf angular velocity signal, the occurrence of leg movement is detected in real time, and the gait cycle is accurately divided to reduce the influence of the signal unrelated to the behavior on the recognition process. In addition, this study proposes a gait behavior recognition method based on event-driven strategies. This method uses a gyroscope as the main sensor and uses a wearable sensor node to collect the angular velocity signals of the legs and waist. In addition, this study analyzes the performance of the algorithm proposed by this paper through experimental research. The comparison results show that the method proposed by this paper has improved the number of recognition action types and accuracy and has certain advantages from the perspective of computation and scalability.


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