Deformation invariant and contactless palmprint recognition using convolutional neural network
Proceedings of the 3rd International Conference on Human-agent Interaction, 2015•dl.acm.org
Palmprint recognition is a challenging problem, mainly due to low quality of the patterns,
variation in focal lens distance, large nonlinear deformations caused by contactless image
acquisition system, and computational complexity for the large image size of typical
palmprints. This paper proposes a new contactless biometric system using features of palm
texture extracted from the single hand image acquired from a digital camera. In this work, we
propose to apply convolutional neural network (CNN) for palmprint recognition. The results …
variation in focal lens distance, large nonlinear deformations caused by contactless image
acquisition system, and computational complexity for the large image size of typical
palmprints. This paper proposes a new contactless biometric system using features of palm
texture extracted from the single hand image acquired from a digital camera. In this work, we
propose to apply convolutional neural network (CNN) for palmprint recognition. The results …
Palmprint recognition is a challenging problem, mainly due to low quality of the patterns, variation in focal lens distance, large nonlinear deformations caused by contactless image acquisition system, and computational complexity for the large image size of typical palmprints. This paper proposes a new contactless biometric system using features of palm texture extracted from the single hand image acquired from a digital camera. In this work, we propose to apply convolutional neural network (CNN) for palmprint recognition. The results demonstrate that the extracted local and general features using CNN are invariant to image rotation, translation, and scale variations.
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