<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Computational intelligence firmly made its way into the areas of consumer applications, banking, education, social networks, and security. Among all the applications, biometric systems play a significant role in ensuring an uncompromised and secure access to resources and facilities. This article presents a first multimodal biometric system that combines KINECT gait modality with KINECT face modality utilizing the rank level and the score level fusion. For the KINECT gait modality, a new approach is proposed based on the skeletal information processing. The gait cycle is calculated using three consecutive local minima computed for the distance between left and right ankles. The feature distance vectors are calculated for each person’s gait cycle, which allows extracting the biometric features such as the mean and the variance of the feature distance vector. For Kinect face recognition, a novel method based on HOG features has been developed. Then, K-nearest neighbors feature matching algorithm is applied as feature classification for both gait and face biometrics. Two fusion algorithms are implemented. The combination of Borda count and logistic regression approaches are used in the rank level fusion. The weighted sum method is used for score level fusion. The recognition accuracy obtained for multi-modal biometric recognition system tested on KINECT Gait and KINECT Eurocom Face datasets is 93.33% for Borda count rank level fusion, 96.67% for logistic regression rank-level fusion and 96.6% for score level fusion.</p></abstract>

Computational intelligence firmly made its way into the areas of consumer applications, banking, education, social networks, and security. Among all the applications, biometric systems play a significant role in ensuring an uncompromised and secure access to resources and facilities. This article presents a first multimodal biometric system that combines KINECT gait modality with KINECT face modality utilizing the rank level and the score level fusion. For the KINECT gait modality, a new approach is proposed based on the skeletal information processing. The gait cycle is calculated using three consecutive local minima computed for the distance between left and right ankles. The feature distance vectors are calculated for each person’s gait cycle, which allows extracting the biometric features such as the mean and the variance of the feature distance vector. For Kinect face recognition, a novel method based on HOG features has been developed. Then, K-nearest neighbors feature matching algorithm is applied as feature classification for both gait and face biometrics. Two fusion algorithms are implemented. The combination of Borda count and logistic regression approaches are used in the rank level fusion. The weighted sum method is used for score level fusion. The recognition accuracy obtained for multi-modal biometric recognition system tested on KINECT Gait and KINECT Eurocom Face datasets is 93.33% for Borda count rank level fusion, 96.67% for logistic regression rank-level fusion and 96.6% for score level fusion.

Score Level and Rank Level Fusion for Kinect-Based Multi-Modal Biometric System

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Score Level and Rank Level Fusion for Kinect-Based Multi-Modal Biometric System"}

Computational intelligence firmly made its way into the areas of consumer applications, banking, education, social networks, and security. Among all the...

Score Level and Rank Level Fusion for Kinect-Based Multi-Modal Biometric System

Online veröffentlicht: 09. Mai 2019

Seitenbereich: 167 - 176

Eingereicht: 06. Feb. 2018

Akzeptiert: 27. Sept. 2018

DOI: https://doi.org/10.2478/jaiscr-2019-0001

Schlüsselwörter
Lane-Emden equations, simulated annealing, legendre polynomials, neural network

© 2019 Md Wasiur Rahman et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Score Level and Rank Level Fusion for Kinect-Based Multi-Modal Biometric System

Online veröffentlicht: 09. Mai 2019

Seitenbereich: 167 - 176

Eingereicht: 06. Feb. 2018

Akzeptiert: 27. Sept. 2018

DOI: https://doi.org/10.2478/jaiscr-2019-0001

SchlüsselwörterLane-Emden equations, simulated annealing, legendre polynomials, neural network

© 2019 Md Wasiur Rahman et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Lane-Emden equations, simulated annealing, legendre polynomials, neural network