Segmentation and Recognition Model for Complex Action Sequences

Geng, Hongyang; Huan, Zhan; Liang, Jiuzhen; Hou, Zhenjie; Lv, Shiyun; Wang, Yunliang

doi:10.1109/jsen.2022.3144157

Cited by 6 publications

(2 citation statements)

References 59 publications

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“…The accuracy is tested using three different biometric identifiers: the finger, the palm, and the iris. Fingerprint and palm print security systems [9] have been presented as a model. The features are extracted using a Gabor filter.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Biometric Based Person Identification Using Machine Learning

N¹

2022

Journal of Pharmaceutical Negative Results

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When compared to the more traditional methods of authentication, biometric systems offer a much higher level of protection for a wide range of uses (like pin, passwords etc.). Various sectors of modern society can find use for biometric systems. Among these are authentication for computers, attendance tracking for businesses, financial transactions, safeguarding private information, securing access to buildings, and ensuring the safety of travellers at airports. Identifying and verifying individuals via their unique physical and behavioural characteristics is the primary function of the biometric system. Importance of biometric systems in modern society is analysed in this paper. Single-trait biometric user recognition is currently used, but it does not offer sufficient security for critical programmes. Multimodal biometric systems are used to get around these issues. Physical and behavioural characteristics, like fingerprints and DNA, signatures and fingerprints, etc., are all part of a multimodal biometric system's arsenal for authenticating users. In addition to the hard biometric features already mentioned (skin colour, age, height, hair colour, eye colour, gender, etc.), the use of a person's soft biometric traits is becoming increasingly common. While soft biometrics can be used to boost the efficiency of biometric systems that focus on other characteristics, they are not without their own set of drawbacks, such as a lack of permanence and distinctive behaviour. User authentication, security, and performance are all boosted by the work presented in this paper using Machine learning (F-SVM -Fuzzy-Support Vector Machine) model.

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Section: Literature Surveymentioning

confidence: 99%

Hybrid Biometric Based Person Identification Using Machine Learning

N¹

2022

Journal of Pharmaceutical Negative Results

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“…A number of novel sensors with built-in IoT solutions for (wireless) data transfer to the cloud are receiving more and more attention. Among the others, the compact inertial measurement units (IMU), which embed tri-axial accelerometers, gyroscopes, and magnetic field sensors, are widely integrated into smartphones, smartwatches, or sold as nice wearable accessories, that are aimed at monitoring biological and bio-mechanical parameters, and are perhaps the most promising; the compact inertial measurement units (IMU), are in general widely used to measure kinematic parameters [1,2].…”

Section: Introductionmentioning

confidence: 99%

Application Scenarios for Gait Analysis with Wearable Sensors and Machine Learning

D’Arco¹,

Guerritore²,

Tedesco³

2022

Proceedings of the 25th IMEKO TC4 International Symposium and 23rd International Workshop on ADC and DAC Modelling and Testing

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The digital revolution that characterizes the beginning of the 4.0 Era has already prompted out a variety of smart living technologies, which rely on the pervasive connectivity granted by the Internet of Things. These technologies are having a relevant impact on health systems, working and domestic environments, sports and rehabilitation, by enabling new promising practices for human body kinematic studies. This paper provides a specific discussion on how kinematic studies in clinical diagnosis, rehabilitation and sport, take benefit from the use of the recent smart living technologies. More specifically, in exploring the latest trends in the application of gait analysis using wearable sensors and Machine Learning techniques.

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