Optimal Face Templates - The Next Step in Surveillance Face Recognition

Optimal Face Templates - The Next Step in Surveillance Face Recognition

Článek WoS

The paper deals with surveillance face recognition in security applications such as surveillance camera systems or access control systems. Presented research is focused on enhancing recognition performance, reducing classification time and memory requirements. We aim to make it feasible to implement face recognition in end devices such as cameras, identification terminals or popular IoT devices. Therefore we utilize algorithms that require low computational power and optimize them in order to reach higher recognition rates. We present a novel higher quantile method that enhances recognition performance via creation of robust and representative face templates for nearest neighbor classifier. Templates computed by the higher quantile method are determined by tolerance intervals which handle feature variability caused by face pose, expression, illumination and possible low image quality. The recognition performance evaluation has been conducted on images captured by surveillance camera system that are contained in unique IFaViD dataset. The IFaViD is the only one dataset captured by real surveillance camera system containing complex scenarios. The results show that the higher quantile method outperforms the contemporary approaches by 4% respectively 10% depending on the IFaViD's test subset.

The paper deals with surveillance face recognition in security applications such as surveillance camera systems or access control systems. Presented research is focused on enhancing recognition performance, reducing classification time and memory requirements. We aim to make it feasible to implement face recognition in end devices such as cameras, identification terminals or popular IoT devices. Therefore we utilize algorithms that require low computational power and optimize them in order to reach higher recognition rates. We present a novel higher quantile method that enhances recognition performance via creation of robust and representative face templates for nearest neighbor classifier. Templates computed by the higher quantile method are determined by tolerance intervals which handle feature variability caused by face pose, expression, illumination and possible low image quality. The recognition performance evaluation has been conducted on images captured by surveillance camera system that are contained in unique IFaViD dataset. The IFaViD is the only one dataset captured by real surveillance camera system containing complex scenarios. The results show that the higher quantile method outperforms the contemporary approaches by 4% respectively 10% depending on the IFaViD's test subset.

Template creation, multiple training images, surveillance face, recognition, classier training, classier learning

Template creation, multiple training images, surveillance face, recognition, classier training, classier learning

MALACH, T.; POMĚNKOVÁ, J.

2021

29.05.2020

Springer Link

1433-7541

PATTERN ANALYSIS AND APPLICATIONS

23

2

Spojené království Velké Británie a Severního Irska

1021

1032

12

https://link.springer.com/article/10.1007/s10044-019-00842-y