The main purpose of the classification of fingerprints is to devise a formula by which a given collection of fingerprints can be tracked and registered. To accelerate the system for classifying fingerprints, it is necessary to utilize fingerprint image characteristics and avoid the different fingerprint forms arising from fingerprint rotation. This paper presents a simple, new approach to the extraction of characteristics from fingerprint images. The proposed method demonstrates that, for a given image, the features remain constant even after being subjected to a wide range of rotations; thus, it creates an array of characteristics which can be used to identify a person from their fingerprint. To achieve this goal, a basic hit-and-miss operation with different structural components is used to detect and count various features in the fingerprint picture; these features are directly identified based on the texture of the fingerprint. The chosen features are used to index the finger image by generating a frequency of occurrences for each one, such that every fingerprint is represented as a vector of these features. The application of the proposed method shows efficient utilization of execution time and memory usage.

[1] Al-Dwairi M., Hendi A., Soliman M., Alqadi Z., “A New Method for Voice Signal Features Creation,” International Journal of Electrical and Computer Engineering, vol. 9, no. 5, pp. 4092-4098, 2019. DOI:10.11591/ijece.v9i5.pp4077-4091 A Simple and Stable Method of Creating Fingerprint Features with Image Rotation 691

[2] Ali M., Mahale V., Yannawar P., and Gaikwad A., “Overview of Fingerprint Recognition System,” in Proceeding of International Conference on Electrical, Electronics, and Optimization Techniques, Chennai, pp. 1334- 1338, 2016. doi: 10.1109/ICEEOT.2016.7754900.

[3] Al-laham M., “Achieving a Secure Information System by Creating Person-Word-Sound Signature,” International Journal of Computer Science and Network Security, vol. 21, no. 10, pp. 219-223, 2021.

[4] Bakshi A. and Gupta S., “Face Anti-Spoofing System using Motion and Similarity Feature Elimination under Spoof Attacks,” The International Arab Journal of Information Technology, vol. 19, no. 5, pp. 747-758, 2022, doi: 10.34028/iajit/19/5/6.

[5] Bazen A., Fingerprint Identification-Feature Extraction, Matching, and Database Search, Twente University Press, 2002.

[6] Cao K., Liu E., and Jain A., “Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine Ridge Structure Dictionary,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 9, pp. 1847-1859, 2014. doi: 10.1109/TPAMI.2014.2302450.

[7] Chen Y. and Jain K., “Beyond Minutiae: A Fingerprint Individuality Model with Pattern, Ridge and Pore Features,” in Proceeding of International Conference on Biometrics, Alghero, pp. 523-533, 2009. doi: 10.1007/978- 3-642-01793-3_54.

[8] Cherrat E., Alaoui R., and Bouzahir H., “Improving of Fingerprint Segmentation Images Based on K-Means and DBSCAN Clustering,” International Journal of Electrical and Computer Engineering, vol. 9, no. 4, pp. 2425- 2432, 2019. doi: 10.11591/ijece.v9i4.

[9] Depeursinge A., Fageot J., Andrearczyk V., Ward J., and Unser M., “Rotation Invariance and Directional Sensitivity: Spherical Harmonics Versus Radiomics Features,” in Proceeding of International Workshop on Machine Learning in Medical Imaging, pp. 107-115, 2018. doi: 10.1007/978-3-030-00919-9_13.

[10] Gnanasivam P. and Muttan S., “An Efficient Algorithm for Fingerprint Preprocessing and Feature Extraction,” Procedia Computer Science, vol. 2, pp. 133-142, 2010. doi: 10.1016/j.procs.2010.11.017.

[11] Jindal R. and Singla S., “Latent Fingerprint Recognition using Hybrid Ant Colony Optimization and Cuckoo Search,” The International Arab Journal of Information Technology, vol. 20, no. 1, pp. 19-28, 2023. doi: 10.34028/iajit/20/1/3

[12] Kaur R., Sandhu P., and Kamra A., “A Novel Method for Fingerprint Feature Extraction,” in Proceeding of International Conference on Networking and Information Technology, Manila, pp. 1-5, 2010. doi: 10.1109/ICNIT.2010.5508569.

[13] Kaya Y., Kuncan M., Kaplan K., Minaz M., and Ertunç H., “Classification of Bearing Vibration Speeds Under 1D-LBP Based on Eight Local Directional Filters,” Soft Computing, vol. 24, no. 16, pp. 12175-12186, 2020. doi: 10.1007/s00500-019- 04656-2.

[14] Khrisat M., Abu Zneit R., Zaini H., and Alqadi Z., “Analysis Methods Used to Extract Fingerprints Features,” Traitement du Signal, vol. 38, no. 3, pp. 711-717, 2021. doi: 10.18280/ts.380318.

[15] Khrisat M., Hindi A., Alqadi Z., and Dwairi M., “Valuable Wavelet Packet Information To Analyze Color Images,” International Journal of Current Advanced Research, vol. 9, no. 2, pp. 21252-21255, 2020. doi: 10.24327/ijcar.2020..21255.4171.

[16] Macfarlane F., Murray P., Marshall S., Perret B., Evans A., and White H., “Robust Object Detection in Colour Images Using a Multivariate Percentage Occupancy Hit-or-Miss Transform,” the journal Mathematical Morphology-Theory and Applications, vol. 5, no. 1, pp. 128-152, 2021. doi: 10.1515/mathm-2020-0111.

[17] Maltoni D., Maio D., Jain A., and Prabhaker S., Handbook of Fingerprint Recognition, Springer Science and Business Media, 2009.

[18] Ross A., Jain A., and Reisman J., “A Hybrid Fingerprint Matcher,” Pattern Recognition, vol. 36, no. 7, pp. 1661-1673, 2003. doi: 10.1016/S0031- 3203(02)00349-7.

[19] Song J., Kim W., and Park K., “Finger-Vein Recognition Based on Deep DenseNet Using Composite Image,” IEEE Access, vol. 7, pp. 66845- 66863, 2019. doi: 10.1109/ACCESS.2019.2918503.

[20] Sudiro S., Paindavoine M., and Kusuma T., “Image Enhancement in Simple Fingerprint Minutiae Extraction Algorithm Using Crossing Number on Valley Structure,” in Proceeding of International Conference on Intelligent and Advanced Systems, Kuala Lumpur, pp. 655-659, 2007. doi: 10.1109/ICIAS.2007.4658469.

[21] Yang J., Shin J., Min B., Bin Park J., and Park D., “Fingerprint Matching Using Invariant Moment Fingercode and Learning Vector Quantization Neural Network,” in Proceeding of International Conference on Computational Intelligence and Security, Guangzhou, pp. 735-738, 2006. doi: 10.1109/ICCIAS.2006.294231.

[22] Yang W., Wang S., Hu J., Zheng G., and Valli C., “Security and Accuracy of Fingerprint-Based Biometrics: A Review,” Symmetry, vol. 11, no. 2, pp. 141, 2019. doi: 10.3390/sym11020141. 692 The International Arab Journal of Information Technology, Vol. 20, No. 4, July 2023

[23] Zaini H., “Analysis of Wavelet Packet Tree Decomposition to Create Image Pattern to be Used in Image Classification System,” International Journal of Computer Science and Mobile Computing, vol. 10, no. 4, pp. 6-16, 2021. doi: 10.47760/ijcsmc.2021.v10i04.002.

[24] Zhao G. and Matti P., “Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no .6, pp. 915-928, 2007. DOI: 10.1109/TPAMI.2007.1110