Iris Liveness Detection Using Multiple Deep Convolution Networks

Big Data and Cognitive Computing

卷:6

Biswajeet Pradhan¹  Shilpa Gite²  Smita Khade² 

[1] Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 20017, Australia;

[2] Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India;

关键词: iris;    liveness detection;    biometric identification pre-trained networks;    VGG16;    Inceptionv3;    Resnet50;   

DOI  :  10.3390/bdcc6020067

来源: DOAJ

【 摘 要 】

In the recent decade, comprehensive research has been carried out in terms of promising biometrics modalities regarding humans’ physical features for person recognition. This work focuses on iris characteristics and traits for person identification and iris liveness detection. This study used five pre-trained networks, including VGG-16, Inceptionv3, Resnet50, Densenet121, and EfficientNetB7, to recognize iris liveness using transfer learning techniques. These models are compared using three state-of-the-art biometric databases: the LivDet-Iris 2015 dataset, IIITD contact dataset, and ND Iris3D 2020 dataset. Validation accuracy, loss, precision, recall, and f1-score, APCER (attack presentation classification error rate), NPCER (normal presentation classification error rate), and ACER (average classification error rate) were used to evaluate the performance of all pre-trained models. According to the observational data, these models have a considerable ability to transfer their experience to the field of iris recognition and to recognize the nanostructures within the iris region. Using the ND Iris 3D 2020 dataset, the EfficeintNetB7 model has achieved 99.97% identification accuracy. Experiments show that pre-trained models outperform other current iris biometrics variants.

【 授权许可】

Unknown