[雙語(yǔ)翻譯]指紋識(shí)別外文翻譯--利用指紋重建技術(shù)的高效自動(dòng)考勤系統(tǒng)

1、2900 英文單詞， 英文單詞，1.7 萬(wàn)英文字符，中文 萬(wàn)英文字符，中文 4900 字文獻(xiàn)出處： 文獻(xiàn)出處：Ramakrishnan J, Ramakrishnan M. An Efficient Automatic Attendance System Using Fingerprint Reconstruction Technique[J]. arXiv preprint arXiv:1208.1672, 2012.An Effic

2、ient Automatic Attendance System Using Fingerprint Reconstruction TechniqueJosphineleela.R，Dr.M.RamakrishnanAbstractBiometric time and attendance system is one of the most successful applications of biometric technology.

3、 One of the main advantage of a biometric time and attendance system is it avoids “buddy-punching“. Buddy punching was a major loophole which will be exploiting in the traditional time attendance systems. Fingerprint rec

4、ognition is an established field today, but still identifying individual from a set of enrolled fingerprints is a time taking process. Most fingerprint-based biometric systems store the minutiae template of a user in the

5、 database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. This belief has now been shown to be false; several algorithms have been p

6、roposed that can reconstruct fingerprint images from minutiae templates. In this paper, a novel fingerprint reconstruction algorithm is proposed to reconstruct the phase image, which is then converted into the grayscale

7、image. The proposed reconstruction algorithm reconstructs the phase image from minutiae. The proposed reconstruction algorithm is used to automate the whole process of taking attendance, manually which is a laborious an

8、d troublesome work and waste a lot of time, with its managing and maintaining the records for a period of time is also a burdensome task. The proposed reconstruction algorithm has been evaluated with respect to the succe

9、ss rates of type-I attack (match the reconstructed fingerprint against the original fingerprint) and type-II attack (match the reconstructed fingerprint against different impressions of the original fingerprint) using a

10、commercial fingerprint recognition system. Given the reconstructed image from our algorithm, we show that both types of attacks can be effectively launched against a fingerprint recognition system.Keywords：Fingerprint Re

11、construction, attendance management system, Minutiae ExtractionI. INTRODUCTION (HEADING 1)Fingerprint reconstruction is one of the most well- known and publicized biometrics. Because of their uniqueness and consistency o

12、ver time, fingerprints have been used for identification over a century, more recently becoming automated due to advancements in computed capabilities. Fingerprint reconstruction is popular because of the inherent ease

13、 of acquisition, the numerous sources (e.g. ten fingers) available for collection, and their established use and collections by law enforcement and immigration.Minutiae-based fingerprint matching algorithm [1] has been p

14、roposed to solve two problems: correspondence and similarity computation. For the correspondence problem, use an alignment-based greedy matching algorithm to establish the correspondences between minutiae.Cryptographic t

15、echniques are being widely used for ensuring the secrecy and authenticity of information. Although several cryptosystems have proven security guarantees (e.g., AES and RSA), the security relies on the assumption that the

16、 cryptographic keys are known only to the legitimate user. Maintaining the secrecy of keys is one of the main challenges in practical elasticity, and rotational effects, which occur during the acquisition. The level of d

17、istortion increases from the center towards the outer regions. The existing approaches for fingerprint matching are: minutiae–based, and correlation-based. The former has several advantages over the latter such as lower

18、time complexity, better space complexity, less requirement of hardware etc.The uniqueness of a fingerprint is due to unique pattern shown by the locations of the minutiae points– irregularities of a fingerprint–ridge end

19、ings, and bifurcations. A novel minutiae-based approach [4], has been proposed to match fingerprint images using similar structures. Distortion poses serious threats through altered geometry, increases false minutiae, an

20、d hence makes it very difficult to find a perfect match. This algorithm divides fingerprint images into two concentric circular regions – inner and outer – based on the degree of distortion. The algorithm assigns weight

21、ages for a minutiae–pair match based on the region in which the pair exists. The algorithm has two stages. In the first stage, the minutiae points are extracted, and in the second stage, the aligning and the matching of

22、the fingerprint images are done. The algorithm is designed to reduce time taken in aligning, immediately after the calculation of the binary image.Recent advances in automated fingerprint identification technology, coupl

23、ed with the growing need for reliable person identification have resulted in an increased use of fingerprints in both government and civilian applications such as border control, employment background checks, and secure

24、facility access. In [5], Quadratic differentials naturally define analytic orientation fields on planar surfaces. This method proposed model orientation fields of fingerprints by specifying quadratic differentials which

25、is used for reliable person identification. Models for all fingerprint classes such as arches, loops and whorls are laid out. These models are parameterized by few, geometrically interpretable parameters which are invari

26、ant under Euclidean motions. Potential applications of these models are the use of their parameters as indices of large fingerprint databases, as well as the definition of intrinsic coordinates for single fingerprint ima

27、ges. The accuracy of models is still challenging task for arches.General characteristics of the fingerprint emerge as the skin on the fingertip begins to differentiate. Fingerprint recognition systems have the advantages

28、 of both ease of use and low cost. Because among various biometric identifiers, such as face, signature, and voice, the fingerprint has one of the highest levels of distinctiveness and performance and it is the most com

29、monly used biometric modality. Haiyun Xu et. al., [6], proposed a novel method to represent minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become trans

30、lations, so that they can be easily compensated for recognition. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector

31、. This method introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evalu

32、ated using two correlation-based spectral minutiae matching algorithms. The performance can be improved by using a fusion scheme and singular points. The spectral minutiae representation overcomes the drawbacks of the mi

33、nutiae sets, thus broadening the application of minutiae- based algorithms. The minutiae extractor is not reliable it affects the efficiency of spectral minutiae representation.Automated Fingerprint Identification System

34、s (AFISs) have played an important role in many forensics and civilian applications. There are two main types of searches in forensics AFIS: ten print search and latent search. In ten print search, the rolled or plain fi