Biometric identification with improved efficiency using sift algorithm

Kaur Parmdeep, Kainth Nitika, International Journal of Advance Research, Ideas and Innovations in Technology.
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Biometric Identification with Improved Efficiency Using Sift
Algorithm
Parmdeep Kaur
Baba Farid College of Engineering and Technology, Bhatinda
parmbawa18@gmail.com
Nitika Kainth
Baba Farid College of Engineering and Technology, Bhatinda
nitika.cdac@gmail.com
Abstract: Biometric technologies are automated methods of identification or verification. The shortest identification time is the
vital need. The identity of a person is based on behavioral and physiological characteristics. Biometric authentication
techniques are generally done by machine generally (but not always). This paper explains increasing the efficiency of a
biometric system using SIFT algorithm. SIFT guarantees the highest potential detection efficiency than all other existing
techniques.
Keywords: Biometrics, Fingerprint, SIFT, Efficiency, Identification Time.
I. INTRODUCTION
Biometrics has become a crucial need of today’s era. It is the science of establishing the identity of person based on physiological
or behavioral traits. It is stimulating substitute to traditional validation systems like passwords etc. To enhance the security, we
can combine different biometric traits called multi biometrics like face and fingerprint or speech and signature and so on.
Fig 1.Instances of few biometric traits related to any individual (a) Fingerprint (b) Hand geometry (c) Iris (d) Speech (e) Teeth (f) Face
Our main objective is to reduce the identification time and increase the efficiency. And we are working on fingerprint
identification using SIFT features. The fingerprint is one of the dominant traits that keeps spreading out due to its uniqueness,
acceptability and low cost. Due to high demand on fingerprint deployments, fingerprint database is supposed to contain a huge
number of enrolled users. In the large identification deployment, the database size becomes larger and then the identification time
will be longer. Due to related system’s performance issues, reducing the identification time is a highly demanding issue.

Fig 2.General Scheme of Biometric System
Finally, we apply SIFT for the same. SIFT stands for Scale Invariance Feature Transform. It efficiently extracts reliable features
and so it is used to overcome different image degradations such as noise, partiality, and rotations. Since Sift does not need any
prior knowledge about traits, the results of the evaluation with SIFT features are expected to show general properties. The
authentication of the results is based on correspondence between features of test image and those of original image in the database
.
Fig 3 Flowchart of SIFT Algorithm
LITERATURE SURVEY
I. Mouad M.H. Ali et al. (2016) is an overview of current research based on fingerprint recognition. The paper is a
brief review of the conceptual and structure of fingerprint recognition. The aim of this paper is to review various
recent works on fingerprint recognition stages step by step and gives summaries of fingerprint databases with
characteristics.
II. Ali Ismail Awad et al. (2012) proposed a methodology of applying MSM to SIFT features. The processing time
and accuracy are reported and compared against the linear search. In the evaluation results, it is confirmed that
the number of matching processes is reduced and the error rate is not increased by MSM.
III. Subba Reddy Borra et al. (2016) studied on the different features of fingerprint recognition systems. Broad
categories of fingerprint patterns and consequently minutae based method is presented. The different approaches
that are based on pattern recognition, wavelet and wave atom are studied. Also, different fingerprint image
improvement technology is presented.
IV. Florin Alexandru Pavel et al. (2009) proposed a new technique to reduce the number of key points so as to improve
the matching efficiency and recognition performance. In addition, in order to further achieve viewpoint
independence for reliable recognition,3D object models are constructed to accurately capture the spatial

relationship between features.
V. Aythami Morales et al. (2010) systematically examined the issues related to contactless palmprint authentication
and presents performance evaluation on two public databases. Results suggest that SIFT performed much better
than the most promising approach OLOF (Orthogonal Line Ordinal Features) employed earlier. Further, the
combination of robust feature matching scores along with those from OLOF can be applied to achieve more
efficiency.
VI. Jian Wu et al. (2013) This paper systematically analyzed the major members of the SIFT family, including SIFT,
PCA-SIFT, GSIFT, CSIFT, SURF, and ASIFT. They are image local feature description algorithms based on
scale-space. Their performance is evaluated in different situations: scale and rotation change, blur change,
illumination change, and affine change. Because of large computation of SIFT and its variants, they investigated
their time consumption empirically in different ways.
VII. Sudhakar .K et al. (2014) This paper explains an efficient method to detect Copy-Move Forgery using SIFT features
and the volume of these features is heavily reduced by Chan-Vese’s Level Set Approach. Multiple forged object
detection, invariant to scale and rotation, high speed, its simplicity in implementation and robustness are some of this
method’s strengths.
VIII. Ramesh Chand Pandey, et al., (2014) In this paper a new method is proposed which uses SURF and SIFT to detect
copy-move forgery in an image based on passive forensic scheme. The proposed method uses SURF and SIFT,
which makes it very fast and robust in detecting copy moved regions. Experimental results demonstrate
commendable performance in image copy-move forgery detection.
IX. Nagesh Kumar et. al. (2010) has taken the above-used algorithms and their methodology for an efficient multimodal
biometric face recognition using speech signal into a new application of plastic surgery. In which speaker identity
was correlated with the physiological and behavioral characteristics of the speaker. As well as in this it combined
plastic surgery face image and speech information in order to improve the problem of multimodal biometric face
recognition system.
II. PROPOSED METHODOLOGY
In this paper, we have used SIFT methodology which can be useful to complete the given problem.
A. Scale-space extrema detection
The first stage of computation searches over all scales and image locations. It is implemented efficiently by using a difference-
of-Gaussian function to identify potential interest points that are invariant to scale and orientation. The image is convolved with
Gaussian filters at different scales and then the difference of successive Gaussian-blurred images are taken. Key points are taken as
maxima/minima of the Difference of Gaussians (DoG) that occur at multiple scales.
B. Keypoint Localisation
Scale-space extrema detection produces too many key point candidates, some of which are unstable. The next step is to perform
a detailed fit to the nearby data for accurate location, scale, and the ratio of principal curvatures. This information allows points
to be rejected that have low contrast (and are therefore sensitive to noise) or are poorly localized along an edge. This includes
interpolation of nearby data for accurate position to accurately determine its position. The initial approach was to just locate each
key point at the location and scale of the candidate key point. The new approach calculates the interpolated location of the
extremum. The second step is discarding low contrast key points using second order Taylor expansion. And finally the elimination
of edge responses. The DoG function will have strong responses along edges, even if candidate key point is not robust to small
amounts of noise
C. Orientation Assignment
In this step, each key point is assigned one or more orientations based on local image gradient directions. This is the key step in
achieving invariance to the rotation as the key point descriptor can be represented relative to this orientation and therefore achieve
invariance to image rotation. One or more orientations are assigned to each key point location based on local image gradient
directions. All future operations are performed on image data that has been transformed relative to the assigned orientation, scale,
and location for each feature, thereby providing invariance to these transformations.
D. Keypoint Descriptor
Previous steps found key point locations at particular scales and assigned orientations to them. This ensured invariance to image
location, scale, and rotation. Now we want to compute a descriptor vector for each key point such that the descriptor is highly
distinctive and partially invariant to the remaining variations such as illumination,3D viewpoint etc. The local image gradients are
measured at the selected scale in the region around each key point. These are transformed into a representation that allows for
significant levels of local shape distortion and change in illumination.

DEVELOPMENT OF THE SIFT ALGORITHM
It is done by applying the following steps in a serial manner:
1. Compilation of different images i.e.80 images (here in this case) and creation of the database.
2. Abstraction of the database made by establishing different features like the difference of Gaussian, metric, scale, orientation.
3. SIFT feature recognition using corresponding computations.
4. Selecting input image that we want to predict.
5. Generation of Tbr file of that specific input image.
6. Now start the mapping of the features of that Tbr file with the stored database.
7. Final result calculation of SIFT function.
III. CONCLUSION AND RESULT
Biometric identification using SIFT features has been developed. The main objective behind this is to increase the efficiency by
decreasing the identification time. The average mean square error has also been reduced. As SIFT is one of the popular methods
of image matching and object recognition, so it efficiently extracts reliable features and hence is used to overcome different image
degradations such as noise, partiality, and rotations. And the plots of efficiency and identification time have been shown.
Fig 4.Execution Time and Accuracy plots of first 10 persons

Namgyal Dhondup et al, International Journal of Advance Research, Ideas and Innovations in Technology.
FUTURE SCOPE
As there are numerous weak points in the system, which can be a topic of concern for the future research work. Some of them are
listed below as the future aspects:
(a) Currently, there is no adjustment in zoom or direction of the camera during operation.
(b) Present work is considering the case when the fingerprint is made using a white background. However, the use of black
background may help in improved efficiency and better response.
(c) To make the system more robust, adaptive binarization may be used.
ACKNOWLEDGEMENT
An endeavor is successful only when it is carried out under proper guidance & blessings. I am hereby thankful to Department of
Electronics and Communication Engineering of Baba Farid College of Engineering and Technology, Deon (Bhatinda), Punjab. I
would also thank all Teaching and Non-teaching staff of BFCET, Bhatinda for their kind of co-operation during the course. The
blessings and support of my family and friends have always given me success, I am extremely thankful for their love.
REFERENCES
[1] D. G. Lowe, “Object recognition from local scale-invariant features,” in Proc. IEEE International Conference on
Computer Vision, 1999, pp. 1150–1157
[2] Liang Cheng, Jianya Gong, Xiaoxia Yang “Robust affine invariant feature extraction for image matching” IEEE
GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 5, NO. 2, APRIL 2008.
[3] D.G. Lowe, “Distinctive image features from scale-invariant key points, “International Journal of Computer Vision, vol. 60,
no. 2, pp. 91–110, 2004.
[4] Jian Wu, Zhiming Cui1, Victor S. Sheng, Pengpeng Zhao, Dongliang Su, Shengrong Gong, “A Comparative Study of SIFT
and its Variants” MEASUREMENT SCIENCE REVIEW, Volume 13, No. 3, 2013.
[5] H. Abubacker Siddique “Advanced Touchless Biometric Identification using SIFT Features” International Journal of
Innovative Research in Advanced Engineering (IJIRAE) Volume 1 Issue 4 (May 2014).
[6] J. Chen and Y.-S. Moon, “Using SIFT features in palm print authentication,” in Proc. 19th International Conference on
Pattern Recognition. IEEE, 2008.
[7] A. Morales, M. A. Ferrer, and A. Kumar, “Improved palmprint authentication using contactless imaging,” in Proc. IEEE
Fourth International Conference on Biometrics: Theory Applications and Systems. IEEE, 2010, pp. 1–6. [8] Ramaswamy, G.,
Sreenivasarao, V., Ramesh, P., and Kiran Dr., “A novel approach for human identification through fingerprints”, International
Journal of Computer Applications, 2010, Vol. 4, No. 3, pp. 169-173.
[9] A1-Ani M. S., “A novel thinning algorithm for fingerprint recognition”, International Journal of Engineering Sciences, Feb
2013, Vol. 2, No. 2, pp. 43-48.
[10] Kute, A., and Kumar, V., “Fingerprint recognition system: A review”, International Journal of Electrical, Electronics and
Computer Engineering, Vol. 3, No. 2, pp. 61-66, 2014.
[11] Gayathri, R. Ramamoorthy, P.”Fingerprint and palmprint Recognition Approach based on Multiple Feature extraction”.
European Journal of scientific research. Vol 76, No 4, 2012.
[12] Akhil Vij, Anoop Namboodiri, “Learning Minutiae Neighborhoods: A New Binary Representation for Matching Fingerprints”,
2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014.
[13] F. Chen, X. Huang, J. Zhou, Hierarchical minutiae matching for fingerprint and Palm print identification, IEEE Trans. Image
Process. 22 (2013) 4964– 632 4971.
[14] Y. Ke and R. Sukthankar, “Pca-sift: a more distinctive representation for local image descriptors,” in 2004 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2004, pp.506–513.
[15] S.A. Bakar, M.S. Hitam, and W.N. Yussof, “Content-Based Image Retrieval using SIFT for Binary and Greyscale Images”, in
Proc. IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2013.
[16] Zitova B. and Flusser J., "Image registration methods: A survey," Journal of image and Vision ELSEVIER, Vol. 21, pp. 977-
1000, June. 2003.
[17] Mikolajczyk, K. and Schmid, C., "A performance evaluation of local descriptors," IEEE Trans. Pattern Ana. Mach. Intel,
27(10), 1615-1630, Feb. 2005.
[18] Hui Lin, Peijun Du, "Image registration based on corner detection and affine transformation" International Congress on
Image and Signal Processing (CISP), 2010 3rd,vol.5. , pp: 2184 - 2188, 2010.

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