In this way, this user’s identity can be directly checked by another device at the meeting site. (2) Personal authentication anywhere. For example, selleck chemicals if one person’s palmprint images have been recorded by a digital camera in the police station, the police can search for this person anywhere using other devices such as smart-phones with cameras. Consequently, the PRADD technique is very useful to look for a lost elderly person or a suspect. (3) Palmprint based biometrics in cloud computing. In the cloud computing environment, palmprint based biometrics can become a service in which the technique of PRADD is needed. For example, palmprint recognition can be a service of the cloud computing for personal authentication on a smart-phone. A user can register palmprints using his old smart-phone.
When he buys a new smart-phone, he does need to not register the palmprints again. Also, the registered palmprints captured by the smart-phone can be used for personal authentication in a user’s other consumer electronics products such as a panel PC, or a notebook PC.In order to study the PRADD technique, we create a non-contact palmprint image database using three devices, i.e., one digital camera and two smart-phones. With the widespread application of digital cameras and smart-phones, the PRADD technique will be mainly used in such consumer electronics products, thus they were used to capture palmprint images in this work.The main contributions of our work are as follows: first, it is the first time the problem of PRADD is investigated, which enriches the research on palmprint recognition.
Second, a robust method to calculate the palm width is proposed, which can be effectively used for scale normalization of palmprints. Third, we evaluate the recognition performance for PRADD of three different methods, i.e., subspace learning method, correlation method and orientation coding based method, respectively. Lastly, we create the first PRADD image database.The rest of this Anacetrapib paper is organized as follows: Section 2 describes the image collection and the preprocessing algorithm. Section 3 provides a brief review of some recognition methods. Section 4 reports the experimental results, and Section 5 concludes the whole paper.2.?Palmprint Image Collection and PreprocessingIn this paper, three popular consumer electronics products including one digital camera and two smart-phones were used to collect palmprint images.
As shown in Figure 1, they are the Canon IXUS 950 IS (C950) digital camera, and the Motorola ME525 (M525) and Nokia 5800 XpressMusic (N5800) smart-phones, respectively. The C950 captures images using a CCD sensor with 800 million pixels while the M525 and
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