Abstract

Biometric authentication is one of the most exciting areas in the era of security. Biometric authentication ideally refers to the process of identifying or verifying the user through physiological and behavioral measurements using security processes. Multimodal biometrics are preferred over unimodal biometrics due to the defensive nature of multimodal biometrics. This research introduces a distinct hand database of individuals, which is acquired using a tailored hardware setup. The database contains four biometric traits: dorsal vein, wrist vein, palm vein, and palm print of the same person, which enables the multimodal biometric authentication exploration to create a spoof-proof authorization system. All four biometrics are captured using a single hardware device. The veins are highlighted by lighting up the infrared (IR) light emitting diodes (LEDs) and a complementary metal oxide semiconductor (CMOS) image sensor is used to capture the vein image. Similarly, the palm region is equally illuminated with the array of white LEDs, and the palm print is captured using the CMOS image sensor. The CMOS sensor has a simple structure and uses a single camera. A total of 308 participants were included in the database, resulting in 8336 unique hand vein and palm print images of both hands. Compared the captured database with the existing databases such as PUT, FYO, VERA, and Bosphorus in terms of availability of the traits, number of subjects, total number of images, number of sessions, and gender. Preliminary experiments were conducted on the self-acquired database using an open-source software package named “Orange”. Various performance parameters were measured to construct a cost-effective authentication system. The classification accuracy obtained in k nearest neighbor (kNN), random forest (RF), support vector machine (SVM), neural network (NN), gradient boosting (GB), and logistic regression (LR) algorithms at a 70% learning rate are 99.8%, 99%, 99.8%, 99.8%, 97.7%, and 99.8%, respectively.

Keyword

Biometric authentication, Contrast limited adaptive histogram equalization, Complementary metal oxide semiconductor, Genuine, Hand vein, Imposter, Multimodal biometrics, Near infrared, Orange.

Cite this article

R. BM, Rao KR

Refference

[1][1]Jain AK, Ross AA, Nandakumar K. Introduction to biometrics. Springer Science & Business Media; 2011.

[2][2]Jain AK, Li SZ. Handbook of face recognition. New York: springer; 2011.

[3][3]Bowyer KW, Burge MJ. Handbook of iris recognition. Springer London; 2016.

[4][4]Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of fingerprint recognition. Springer Science & Business Media; 2009.

[5][5]Jain AK, Nandakumar K, Ross A. 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recognition Letters. 2016; 79:80-105.

[6][6]Dahea W, Fadewar HS. Multimodal biometric system: a review. International Journal of Research in Advanced Engineering and Technology. 2018; 4(1):25-31.

[7][7]Li SZ, Jain A. Encyclopedia of biometrics. Springer Publishing Company, Incorporated; 2015.

[8][8]Kumar A, Prathyusha KV. Personal authentication using hand vein triangulation. In biometric technology for human identification V 2008. International Society for Optics and Photonics.

[9][9]Carmeliet P, Jain RK. Angiogenesis in cancer and other diseases. Nature. 2000; 407(6801):249-57.

[10][10]Singh M, Singh R, Ross A. A comprehensive overview of biometric fusion. Information Fusion. 2019; 52:187-205.

[11][11]Bharathi S, Sudhakar R. Biometric recognition using finger and palm vein images. Soft Computing. 2019; 23(6):1843-55.

[12][12]Toygar Ö, Babalola FO, Bitirim Y. FYO: a novel multimodal vein database with palmar, dorsal and wrist biometrics. IEEE Access. 2020; 8:82461-70.

[13][13]Verma SB, Saravanan C. Performance analysis of various fusion methods in multimodal biometric. In international conference on computational and characterization techniques in engineering & sciences 2018 (pp. 5-8). IEEE.

[14][14]Kabir W, Ahmad MO, Swamy MN. Weighted hybrid fusion for multimodal biometric recognition system. In international symposium on circuits and systems 2018 (pp. 1-4). IEEE.

[15][15]Matkowski WM, Chan FK, Kong AW. A study on wrist identification for forensic investigation. Image and Vision Computing. 2019; 88:96-112.

[16][16]Tiong LC, Kim ST, Ro YM. Multimodal facial biometrics recognition: dual-stream convolutional neural networks with multi-feature fusion layers. Image and Vision Computing. 2020.

[17][17]Mason J, Dave R, Chatterjee P, Graham-allen I, Esterline A, Roy K. An investigation of biometric authentication in the healthcare environment. Array. 2020.

[18][18]Das R, Piciucco E, Maiorana E, Campisi P. Convolutional neural network for finger-vein-based biometric identification. IEEE Transactions on Information Forensics and Security. 2018; 14(2):360-73.

[19][19]Al-johania NA, Elrefaei LA. Dorsal hand vein recognition by convolutional neural networks: feature learning and transfer learning approaches. International Journal of Intelligent Engineering and Systems. 2019; 12(3):178-91.

[20][20]Thapar D, Jaswal G, Nigam A, Kanhangad V. PVSNet: Palm vein authentication siamese network trained using triplet loss and adaptive hard mining by learning enforced domain specific features. In international conference on identity, security, and behavior analysis 2019 (pp. 1-8). IEEE.

[21][21]Kim W, Song JM, Park KR. Multimodal biometric recognition based on convolutional neural network by the fusion of finger-vein and finger shape using near-infrared (NIR) camera sensor. Sensors. 2018; 18(7):1-34.

[22][22]Kovac I, Marák P. Multimodal biometric system based on fingerprint and finger vein pattern. Slovak Univ. Technol., Bratislava, Slovakia. 2019.

[23][23]Abdalrady NA, Aly S. Fusion of multiple simple convolutional neural networks for gender classification. In international conference on innovative trends in communication and computer engineering 2020 (pp. 251-6). IEEE.

[24][24]Agbo-ajala O, Viriri S. Deeply learned classifiers for age and gender predictions of unfiltered faces. The Scientific World Journal. 2020.

[25][25]Hassan B, Izquierdo E, Piatrik T. Soft biometrics: a survey. Multimedia Tools and Applications. 2021:1-44.

[26][26]Deng Y, Teng S, Fei L, Zhang W, Rida I. A multifeature learning and fusion network for facial age estimation. Sensors. 2021; 21(13):1-15.

[27][27]Pujari MV, Patil R, Sutar MS. Research paper on biometrics security. Contemporary Research in India.2021.

[28][28]Haider SA, Rehman Y, Ali SM. Enhanced multimodal biometric recognition based upon intrinsic hand biometrics. Electronics. 2020; 9(11):1-20.

[29][29]Bernacki K, Moroń T, Popowicz A. Modified distance transformation for image enhancement in NIR imaging of finger vein system. Sensors. 2020; 20(6):1-9.

[30][30]Yao Q, Song D, Xu X. Robust finger-vein ROI localization based on the 3σ criterion dynamic threshold strategy. Sensors. 2020; 20(14):1-21.

[31][31]Alay N, Al-baity HH. Deep learning approach for multimodal biometric recognition system based on fusion of iris, face, and finger vein traits. Sensors. 2020; 20(19):1-17.

[32][32]Lv GL, Shen L, Yao YD, Wang HX, Zhao GD. Feature-level fusion of finger vein and fingerprint based on a single finger image: the use of incompletely closed near-infrared equipment. Symmetry. 2020; 12(5):1-18.

[33][33]Sarangi PP, Nayak DR, Panda M, Majhi B. A feature-level fusion based improved multimodal biometric recognition system using ear and profile face. Journal of Ambient Intelligence and Humanized Computing. 2021:1-32.

[34][34]Vyas R, Kanumuri T, Sheoran G, Dubey P. Accurate feature extraction for multimodal biometrics combining iris and palmprint. Journal of Ambient Intelligence and Humanized Computing. 2021:1-9.

[35][35]Bala N, Gupta R, Kumar A. Multimodal biometric system based on fusion techniques: a review. Information Security Journal: A Global Perspective. 2021:1-49.

[36][36]Vijayakumar T. Synthesis of palm print in feature fusion techniques for multimodal biometric recognition system online signature. Journal of Innovative Image Processing. 2021; 3(2):131-43.

[37][37]Varshini SA, Aravinth J. Hybrid level fusion schemes for multimodal biometric authentication system based on matcher performance. In computational vision and bio-inspired computing 2021 (pp. 431-47). Springer, Singapore.

[38][38]Leghari M, Memon S, Dhomeja LD, Jalbani AH, Chandio AA. Deep feature fusion of fingerprint and online signature for multimodal biometrics. Computers. 2021; 10(2):1-15.

[39][39]Ghouzali S, Nafea O, Wadood A, Hussain M. Cancelable multimodal biometrics based on chaotic maps. Applied Sciences. 2021; 11(18):1-15.