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Sheeba Rani, S.
- Biometric Authentication System with Hand Vein Features using Morphological Processing
Abstract Views :210 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu,, IN
2 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology,Coimbatore – 641008, Tamil Nadu, IN
4 Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
1 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu,, IN
2 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology,Coimbatore – 641008, Tamil Nadu, IN
4 Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 11, No 26 (2018), Pagination: 1-6Abstract
Objective: In order to prevent the theft of authentication of the keywords and to preserve the biometric authentication a method is derived to secure the pattern. Methods/Statistical analysis: An efficient identification and authentication methods are implemented by using the dorsal vein recognition system which is very popular among the researchers of the world. By identifying the unique pattern of the hand vein, the features are extracted from the images and pattern is framed and dimension reduction is based on the system application. Application: This simple model can be used in reduction of dimensionality and the noise can be removed from the biometric pattern which helps to have high security. Findings: This paper contributes on image acquisition, preprocessing techniques, feature extraction in hand vein authentication system.References
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- Rakesh P, Pankaj B. Implementation of an Efficient Hand Vein Structure Authentication. International Journal on Emerging Technologies. 2017; 8(1):201-204.
- Ananth JP, Balakrishnan S, Premnath SP. Logo Based Pattern Matching Algorithm for Intrusion Detection System in Wireless Sensor Network. International Journal of Pure and Applied Mathematics. 2018; 119(12):753-62.
- Park G, Soowon K. Hand biometric recognition based on fused hand geometry and vascular patterns. Sensors. 2013; 13(3):2895-2910. Crossref PMid:23449119 PMCid:PMC3658721
- Honarpisheh Z, KarimFaez. An efficient dorsal hand vein recognition based on firefly algorithm. International Journal of Electrical and Computer Engineering (IJECE). 2013; 3(1):30-41.
- Kumar A, Venkata Prathyusha K. Personal authentication using hand vein triangulation and knuckle shape. IEEE Transactions on Image processing. 2009; 18(9):2127-36. Crossref PMid:19447728
- Pal MM, Jasutkar RW. Implementation of hand vein structure authentication-based system. Communication Systems and Network Technologies (CSNT), 2012 International Conference on. IEEE. 2012; 2(1):1-3. Crossref
- Wang L, Graham L, Siu-Yeung Cho D. Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern recognition. 2008; 41(3):920-29. Crossref
- Fayyaz M. A novel approach for Finger Vein verification based on self-taught learning. Machine Vision and Image Processing (MVIP), 2015 9th Iranian Conference on. IEEE. 2015; p. 88-91. Crossref Crossref
- Hsu C, Shu-Sheng H, Jen-Chun L. Personal authentication through dorsal hand vein patterns. Optical Engineering. 2011; 50(8):1-11. Crossref
- Sujatha K, Shalini Punithavathani D. Optimized ensemble decision-based multi-focus image fusion using binary genetic Grey-Wolf optimizer in camera sensor networks. Multimedia Tools and Applications. 2018; 77(2):1735-59. Crossref
- Punithavathani DS, Sujatha K, Jain JM. Surveillance of anomaly and misuse in critical networks to counter insider threats using computational intelligence. Cluster Computing. 2015; 18(1):435-51. Crossref
- Vidya R, Raj DV, Sujatha K. Knowledge understanding and advanced searching. ICTACT Journal on Soft Computing. 2017; 7(3):1467-1742. Crossref
- An Efficient and Complete Automatic System for Detecting Lung Module
Abstract Views :201 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
1 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 11, No 26 (2018), Pagination: 1-5Abstract
Objectives: To make a fully automated algorithm that is based on simple and quick steps, which produces consistent output for the same inputs. Methods/Statistical Analysis: For thorax and lung segmentation, region growing based method is used to segment the region of interest. The missing parts of the lungs are reconstructed using morphological operations. After that, nodules are detected based on the features of the reconstructed image. Artificial Neural Network has been used for classifying the images. Findings: An aggregate of 100 pictures with determination of 512 × 512 pixels with eight bits for every shading channel are caught. 90% affectability was obtained with 0.05 false positives for each picture. Application/Improvements: This framework distinguishes the phase of lung malignancy. The outcomes demonstrate that the tumors are of various measurements. By estimating the measurements of the tumor the lung disease stage can be recognized precisely utilizing the proposed technique. The outcomes indicate great potential for lung growth identification at beginning time.References
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- Integrated Anthropometric Approach for Ceaseless Authentication
Abstract Views :164 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
1 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 11, No 26 (2018), Pagination: 1-4Abstract
Objectives: To model a novel ceaseless client validation method to authorize the client regardless of their body position before the capturing system. The system ceaselessly validates the client with their various soft anthropometric parameters such as (e.g. wearables and skin) in addition to hard biometrics. Methods/Statistical Analysis: The proposed system mechanically stores in the soft anthropometric parameters each time the client logs in and integrate the anthropometric parametric features along with the conventional face traits for verification thus fusing the combination of hard and soft biometric attributes to attest a client ceaselessly. The methodology comprises of various modes such as initialization, validation and regeneration. Findings: Various samples of facial colour features and user’s cloth colour features are used as soft biometrics in this system for authorization. The experimental results of AR show the extensive improvement over the existing methods. Application/Improvements: This methodology eliminates the challenges faced in face recognition due to different expressions and postures, lighting effects. Thus the key discriminating features are authenticated using hard and soft biometrics thus making it a high secure technology.References
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- Sim T, Zhang S, Janakiraman R, Kumar S. Continuous verification using multimodal biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007; 29(4):687–700. PMid: 17299225. Crossref.
- Lei Z, Liao S, Pietikainen M, Li SZ. Face recognition by exploring information jointly in space, scale and orientation. IEEE Transactions on Image Processing. 2011; 20(1): 247–57. PMid: 20643604. Crossref.
- Solami EA, Boyd C, Clark A, Ahmed I. User-representative feature selection for keystroke dynamics. 5th International Conference on Network and System Security. 2011; p. 229–33. Crossref.
- Sujatha T, Sangeetha T, Balakrishnan S, Susila N. Honey/sugar template based on biometric protection using bloom filter. International Journal of Pure and Applied Mathematics. 2018; 119(12): 1143–55.