%PDF-1.4 ; Gragnaniello, D.; Sansone, C.; Verdoliva, L.; Marcel, S. The 1st competition on counter measures to finger vein spoofing attacks. After having study of this review we can try to increase the efficiency which will help for further research work also. As the fingerprints are unique, gender classification helps to minimize the large data. ; Lee, H.C.; Park, K.R. Available online: Yin, Y.; Liu, L.; Sun, X. SDUMLA-HMT: A multimodal biometric database. The main objective of this study is to analyze the most recent techniques applied in finger vein identification. those of the individual authors and contributors and not of the publisher and the editor(s). [, Damavandinejadmonfared, S. Finger vein recognition using linear kernel entropy component analysis. Existing segmentation, separation of overlapped fingerprints and enhancement techniques … This paper provided a comprehensive review on conventional, machine learning and deep learning-based finger vein recognition approaches. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. In Proceedings of the 2014 International Work Conference on Bio-inspired Intelligence (IWOBI), Liberia, Costa Rica, 16–18 July 2014; pp. Raghavendra, R.; Busch, C. Presentation attack detection algorithms for finger vein biometrics: A comprehensive study. Yang, J.; Shi, Y.; Jia, G. Finger-vein image matching based on adaptive curve transformation. 87–91. Finger vein recognition using optimal partitioning uniform rotation invariant LBP descriptor. 1020–1023. [])), +((!+[]+(!![])+!![]+!![]+!![]+!![]+!![]+!![]+!![]+[])+(!+[]+(!![])-[])+(!+[]-(!![]))+(!+[]+(!![])+!![]+!![])+(+!![])+(!+[]+(!![])+!![]+!![]+!![]+!![]+!![]+!![]+!![])+(!+[]+(!![])+!![]+!![]+!![]+!![]+!![]+!![])+(!+[]+(!![])-[])+(!+[]+(!![])+!![]))/+((+!![]+[])+(!+[]+(!![])+!![]+!![]+!![])+(!+[]+(!![])+!![]+!![]+!![]+!![]+!![]+!![]+!![])+(!+[]+(!![])+!![]+!![]+!![])+(!+[]+(!![])+!![]+!![]+!![]+!![]+!![]+!![]+!![])+(!+[]+(!![])+!![]+!![]+!![]+!![]+!![])+(!+[]+(!![])+!![]+!![]+!![]+!![])+(!+[]+(!![])+!![]+!![]+!![]+!![])+(!+[]+(!! 410–415. Available online: Zafar, W.; Ahmad, T.; Hassan, M. Minutiae based fingerprint matching techniques. ; Feng, W. Finger vein identification based on 2-D gabor filter. 1–4. This paper presents a short survey that emphasizes the implementations of basic machine learning notions for compensating some fingerprint problems. A minutiae-based fingerprint matching algorithm using phase correlation. ; Lee, H.C.; Park, K.R. Kaur, P.; Verma, P. Human Identification with Finger Veins Using Repeated Line Tracking, Even Gabor and Automatic Trimap Generation Algorithms. Good performance of a finger vein image depends on the finger vein image quality [, Image assessment acts as the first sub-step of preprocessing. Liu, Y.; Ling, J.; Liu, Z.; Shen, J.; Gao, C. Finger vein secure biometric template generation based on deep learning. In Proceedings of the 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), New Delhi, India, 22–24 February 2017; pp. Examiners and other human face “specialists,” including forensically trained facial reviewers and untrained superrecognizers, were more accurate than the control groups on a challenging test of face identification. The basic goal of image enhancement is to advance the interpretable or knowledge of information in images for human viewers or to get the standard enhanced image from the unclear acquired image [, Feature extraction represents one of the most crucial and major steps of FVR. [. [. ; Rizi, S.M. Personal identification for single sample using finger vein location and direction coding. collected and analyzed the corpora used in this paper; G.Y. ; Raut, U.K. endobj In. biometrics; finger vein recognition; feature extraction; matching; performance analysis. In Proceedings of the Asian Conference on Computer Vision, Xi’an, China, 23–27 September 2009; Springer: Berlin/Heidelberg, Germany, 2009; pp. [. During this stage, quality of acquired image samples is examined to estimate the suitability for further processing. ; Zhou, B. Accuracy rate of almost all the proposed machine learning finger vein algorithms is close to 100% [, Deep Learning is a form of machine learning which include multiple layers of learning algorithms. Qin, H.; El-Yacoubi, M.A. ; Abdullah, I.; Seman, K.; Sayuti, N.N.S. Minutiae points are used in finger vein recognition, and such methods are already used in fingerprint recognition technique [, The matching technique is the last step of recognition to decide whether an input image is genuine or an imposter for one enrolled image, in which a matching score is generated. [, Qin, H.; Li, S.; Kot, A.C.; Qin, L. Quality assessment of finger-vein image. 2019-05-31truewww.tandfonline.com10.1080/16168658.2019.1611030www.tandfonline.comtrue2019-05-3110.1080/16168658.2019.1611030 In Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS), Rome, Italy, 16–19 November 2015; pp. In Proceeding of the 4th International Conference on Artificial Intelligence and Computer Science (AICS2016), Langkawi, Malaysia, 28–29 November 2016. Minutiae points refer to the terminal point and bifurcation point of blood vessels, and are one kind of important feature of a finger vein image. Miura, N.; Nagasaka, A.; Miyatake, T. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier. Fake finger-vein image detection based on fourier and wavelet transforms. Xie, S.; Fang, L.; Wang, Z.; Ma, Z.; Li, J. New Finger-vein Recognition Method Based on Image Quality Assessment. [, Xie, C.; Kumar, A. Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing. These kinds of techniques have also proved to be efficient for feature extraction, matching and enhancing the performance of the FVR method. Hence, different imaging devices using the ROI extraction method face challenges, such as gray level, image size variation, and background noise appearances in finger vein images, which affect the performance of the ROI extraction method. In Proceedings of the Chinese Conference on Biometric Recognition, Beijing, China, 3–4 December 2011; Springer: Berlin/Heidelberg, Germany, 2011; pp. ; Khaniabadi, S.M. FVR systems are also vulnerable to presentation attacks from printed vein images. Image processing methods were the finest option for human administration to define fingerprint images. Yang, J.; Shi, Y. Finger–vein ROI localization and vein ridge enhancement. [, Liu, C.; Kim, Y.H. In Proceedings of the 2012 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 23–25 November 2012; pp. ; Mishra, K.N. ; Yoon, H.S. Feature extraction is one of the main steps in FVR. Review on Reliable Pattern Recognition with Machine Learning Techniques Wu, J.D. Algorithms were assessed in the key recognition steps of image acquisition, preprocessing, feature extraction and matching. Vlachos, M.; Dermatas, E. Finger vein segmentation from infrared images based on a modified separable mumford shah model and local entropy thresholding. ; Liu, C.T. The paper presented the most recent research advancements in the field of FVR during the past decade, despite the challenges that need to be resolved. A study of two direction weighted (2D) 2 LDA for finger vein recognition. Numerous conventional finger vein identification methods have been developed, but some methods [, Some machine learning techniques (e.g., SVM, neural network and fuzzy logic) have been used in the feature extraction and matching stage of biometrics. This work is supported by National Science Foundation of China under Grant 61703235, 61472226, and 61573219 and the Key Research and Development Project of Shandong Province under Grant 2018GGX101032.
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