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Detecting Iris Liveness with Batch Normalized Convolutional Neural Network

Min Long1,2,*, Yan Zeng1
Changsha University of Science and Technology, Changsha, 410014, China.
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha, 410014, China.
* Corresponding Author: Min Long. Email: .

Computers, Materials & Continua 2019, 58(2), 493-504. https://doi.org/10.32604/cmc.2019.04378

Abstract

Aim to countermeasure the presentation attack for iris recognition system, an iris liveness detection scheme based on batch normalized convolutional neural network (BNCNN) is proposed to improve the reliability of the iris authentication system. The BNCNN architecture with eighteen layers is constructed to detect the genuine iris and fake iris, including convolutional layer, batch-normalized (BN) layer, Relu layer, pooling layer and full connected layer. The iris image is first preprocessed by iris segmentation and is normalized to 256×256 pixels, and then the iris features are extracted by BNCNN. With these features, the genuine iris and fake iris are determined by the decision-making layer. Batch normalization technique is used in BNCNN to avoid the problem of over fitting and gradient disappearing during training. Extensive experiments are conducted on three classical databases: the CASIA Iris Lamp database, the CASIA Iris Syn database and Ndcontact database. The results show that the proposed method can effectively extract micro texture features of the iris, and achieve higher detection accuracy compared with some typical iris liveness detection methods.

Keywords

Iris liveness detection, batch normalization, convolutional neural network, biometric feature recognition.

Cite This Article

M. Long and Y. Zeng, "Detecting iris liveness with batch normalized convolutional neural network," Computers, Materials & Continua, vol. 58, no.2, pp. 493–504, 2019.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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