___

• Akgül, A., C. Arslan, and B. Arıcıoglu, 2019 Design of an interface for random number generators based on integer and fractional order chaotic systems. Chaos Theory and Applications 1: 1–18.
• Akgül, A., M. Z. Yıldız, Ö. F. Boyraz, E. Güleryüz, S. Kaçar, et al., 2020 Do˘grusal olmayan yeni bir sistem ile damar görüntülerinin mikrobilgisayar tabanlı olarak ¸sifrelenmesi. Journal of the Faculty of Engineering & Architecture of Gazi University 35.
• Akhloufi, M. and A. Bendada, 2008 Hand and wrist physiological features extraction for near infrared biometrics. In 2008 Canadian Conference on Computer and Robot Vision, pp. 341–344, IEEE.
• Boyraz, Ö. F. and M. Z. Yildiz, 2016 Mobil damar görüntüleme cihazı tasarımı. In 4th International Symposium on Innovative Technologies in Engineering and Science (ISITES2016) 3-5 Nov 2016 Alanya/Antalya-Turkey.
• Chai, X., Z. Gan, Y. Chen, and Y. Zhang, 2017 A visually secure image encryption scheme based on compressive sensing. Signal Processing 134: 35–51.
• Cho, S. R., Y. H. Park, G. P. Nam, K. Y. Shin, H. C. Lee, et al., 2012 Enhancement of finger-vein image by vein line tracking and adaptive gabor filtering for finger-vein recognition. In Applied Mechanics and Materials, volume 145, pp. 219–223, Trans Tech Publ.
• Dzwonkowski, M., M. Papaj, and R. Rykaczewski, 2015 A new quaternion-based encryption method for dicom images. IEEE Transactions on Image Processing 24: 4614– 4622.
• Hsiao, H.-I. and J. Lee, 2015 Fingerprint image cryptography based on multiple chaotic systems. Signal Processing 113: 169–181.
• Hua, Z. and Y. Zhou, 2017 Design of image cipher using block-based scrambling and image filtering. Information Sciences 396: 97–113.
• Lee, E., H. Jung, and D. Kim, 2010 Infrared imaging based finger recognition method. In Proceedings of International Conference on Convergence and Hybrid Information Technology, pp. 228–230.
• Niyaz, O., Z. G. Cam, and T. Yildirim, 2017Wrist vein recognition by ordinary camera using phase-based correspondence matching. In Modelling, Identificat. Control, pp. 89– 93.
• Pascual, J. E. S., J. Uriarte-Antonio, R. Sanchez-Reillo, and M. G. Lorenz, 2010 Capturing hand or wrist vein images for biometric authentication using low-cost devices. In 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 318–322, IEEE.
• Raut, S. D., V. Humbe, and A. V. Mane, 2017 Development of biometrie palm vein trait based person recognition system: Palm vein biometrics system. In 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), pp. 18–21, IEEE.
• Rukhin, A., J. Soto, J. Nechvatal, M. Smid, and E. Barker, 2001 A statistical test suite for random and pseudorandom number generators for cryptographic applications. Technical report, Booz-allen and hamilton inc mclean va.
• Stimper, V., S. Bauer, R. Ernstorfer, B. Schölkopf, and R. P. Xian, 2019 Multidimensional contrast limited adaptive histogram equalization. IEEE Access 7: 165437–165447.
• Yildiz, M. Z., O. Boyraz, E. Guleryuz, A. Akgul, and I. Hussain, 2019 A novel encryption method for dorsal hand vein images on a microcomputer. IEEE Access 7: 60850– 60867.
• Yildiz, M. Z. and Ö. F. Boyraz, 2019 Development of a lowcost microcomputer based vein imaging system. Infrared Physics & Technology 98: 27–35.
• Zhang, L.-b., Z.-l. Zhu, B.-q. Yang, W.-y. Liu, H.-f. Zhu, et al., 2015 Medical image encryption and compression scheme using compressive sensing and pixel swapping based permutation approach. Mathematical Problems in Engineering 2015.