Detecting Textured Contact Lens in Uncontrolled Environment Using DensePAD
Autor: | Mayank Vatsa, Afzel Noore, Richa Singh, Naman Kohli, Daksha Yadav |
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Rok vydání: | 2019 |
Předmět: |
021110 strategic
defence & security studies business.industry Computer science media_common.quotation_subject Iris recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 02 engineering and technology Convolutional neural network Contact lens Presentation ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing IRIS (biosensor) Computer vision Artificial intelligence business media_common |
Zdroj: | CVPR Workshops |
Popis: | The ubiquitous use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor conditions. This scenario has necessitated the development of reliable iris recognition algorithms for such an uncontrolled environment. Additionally, iris presentation attacks such as textured contact lens pose a major challenge to current iris recognition systems. Motivated by these, this paper presents two key contributions. First, a new Unconstrained Multi-sensor Iris Presentation Attack (UnMIPA) database is created. It consists of more than 18,000 iris images of subjects wearing textured contact lens and without wearing contact lenses captured in both indoor and outdoor environment using multiple iris sensors. The second contribution of this paper is a novel algorithm, DensePAD, which utilizes DenseNet based convolutional neural network architecture for iris presentation attack detection. In-depth experimental evaluation of this algorithm reveals its superior performance in detecting iris presentation attack images on multiple databases. The performance of the proposed DensePAD algorithm is also evaluated in real-world scenarios of open-set iris presentation attacks which highlights the challenging nature of detecting iris presentation attack images from unseen distributions. |
Databáze: | OpenAIRE |
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