Authors : Boby Kumar; Deepak Sharma; Dr. Navin Kr. Tyagi

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/ymsd24zv

Scribd : https://tinyurl.com/34vrmsp4

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1763

Abstract : The process of automatically identifying a person based on their physiological and behavioural traits is known as biometric recognition. A unimodal or multimodal biometric system might be based on physiological or behavioural traits. Some shortcomings in the unimodal biometric system include spoofing, non- universality, intra-class differences, and noisy data. Using the face and palm images as templates, we are implementing a multimodal biometric system. A person's palm image can provide a wealth of detailed information about them. Palm image have three major lines, name as principle line, secondary line and wrinkles which are unique in nature. Face image have more feature than any other biometric traits. Face image is recognized easily in less time. Combining these two biometric template gives higher security. Message image kept secretly in the cover image called stego image. In this steganography technique we are using palm image as a Message and face image as a cover image. After getting stego image Identification process works. The important featureof identification process id De-Stego. This feature of identification will separate the message image (palm image) from cover image (face image). After separation of images the decision will take care, weather the image identified or notidentified.

Keywords : Steganography Technique De-Stego, Face Image, Palm Image, Stego-Image Verification And Identification.

References :

1. Ammour,   Basma,   et   al.    "Face–iris    multimodal    biometric    identification system."  Electronics 9.1 (2020): 85.
2. Chandran, Saravanan, and Satya Bhushan Verma. "Touchless Palmprint Verification using Shock Filter, SIFT, I-RANSAC, and LPD." IOSR Journal of Computer Engineering 17.3 (2015): 01-08.
3. Joseph, Teena, et al. "A multimodal biometric authentication scheme based on feature fusion for improving security in cloud environment." Journal of Ambient Intelligence and Humanized Computing (2020): 1-9.
4. Karthika, P., R. Ganesh Babu, and K. Jayaram. "Biometric based on steganography image security in wireless sensor networks." Procedia Computer Science 167 (2020): 1291-1299.
5. Mishra, Ashish. "Multimodal biometrics it is: need for future systems." International journal of computer applications 3.4 (2010): 28-33.
6. Mustafa, Ahmed Shamil, Aymen Jalil Abdulelah, and Abdullah Khalid Ahmed. "Multimodal Biometric System Iris and Fingerprint Recognition Based on Fusion Technique." International Journal of Advanced Science and Technology 29 (2020): 7423- 7432.
7. Singh, Arun Kumar, Juhi Singh, and Harsh Vikram Singh. "Steganography in images using lsb technique." International Journal of Latest Trends in Engineering and Technology     (IJLTET) 5.1 (2015): 426-430.
8. Satya Bhushan verma and Saravanan Chandran. "Comparative Study of FAST MSER and Harris for Palmprint Verification System." International Journal of Scientific & Engineering Research 7.12 (2016): 855-858.
9. Satya Bhushan verma, and Chandran Saravanan. "Performance analysis of various fusion methods in multimodal biometric." 2018 International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES). IEEE, 2018.
10. Satya Bhushan verma, and Saravanan Chandran. "Touchless Region based Palmprint Verification System." International Journal of Computer Science and Information Security (IJCSIS) 15.4 (2017).
11. Walia, Gurjit Singh, et al. "Secure multimodal biometric system based on diffused graphs    and optimal score fusion." IET Biometrics 8.4 (2019): 231-242.
12. Xiong, Qi, et al. "A modified chaotic binary particle swarm optimization scheme and its application in face-iris multimodal biometric identification." Electronics 10.2 (2021): 217