With the accelerated advancement of global digitalization, information interconnectivity has become a fundamental characteristic of modern society. During this transformative process, the security vulnerabilities of traditional identity authentication systems have become increasingly prominent: current systems primarily rely on physical credentials and multi-factor password mechanisms. These static verification methods not only suffer from inherent weaknesses such as susceptibility to loss and forgery but may also lead to severe personal privacy breaches and societal security risks due to information leakage. In the face of increasingly sophisticated cybersecurity threats, the development of novel identity authentication technologies has become an urgent necessity for ensuring digital security. As a critical direction in identity authentication, biometric technology verifies identity by analyzing individuals' unique physiological or behavioral characteristics. Among these, iris recognition technology demonstrates significant technical superiority and broad application prospects due to its liveness detection capability, contactless acquisition, high uniqueness, long-term stability, and inherent anti-counterfeiting properties. However, existing iris recognition technologies still face major challenges in practical applications: not all scenarios can provide sufficient computational resources or deployment space. To address this critical issue, this study innovatively proposes a longitudinal gray-scale integration-based iris segmentation method. This approach can operate efficiently on miniaturized PCs or embedded processors while achieving precise center localization, thereby providing a reliable foundation for subsequent iris texture recognition. 

Nan Liu. Research on Rapid Iris Localization Algorithm Based on Projection Analysis. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 8: 1-7. https://doi.org/10.25236/AJCIS.2025.080801.

[1] Brückner R,Batschelet E,Hugenschmidt F.The Basel longitudinal study on aging (1955-1978). Ophthalmo-gerontological research results.[J]. Documenta ophthalmologica. Advances in ophthalmology, 1986,64(3):235- 310.

[2] Mehrotra Hunny, Vatsa Mayank, Singh Richa, Majhi Banshidhar. Does iris change over time?[J]. PloS one,2013,8(11).

[3] Li Kai. Application of Iris Recognition Technology in Mobile Terminal Field[J]. Network Security Technology & Application, 2015, 000(003): 84-86.

[4] Rathnayake R, Madhushan N, Jeeva A, et al. Real-Time Multi-Spectral Iris Extraction in Diversified Eye Images Utilizing Convolutional Neural Networks[J]. IEEE Access, 2024, 12: 93283-93293.

[5] Gao R, Bourlai T. On designing a swiniris transformer based iris recognition system[J]. IEEE Access, 2024, 12: 30723-30737.

[6] Kulkarni V V, Hatture S M, Karchi R P, et al. IRIS and face-based multimodal biometrics systems[C]//International Conference on Data Analytics & Learning. Singapore: Springer Nature Singapore, 2022: 31-47.

[7] Prabhu R, Nagarajan R. ANFFractalNet: Adaptive neuro-fuzzy FractalNet for iris recognition[J]. Biomedical Signal Processing and Control, 2025, 108: 107984.

[8] Daugman J. The importance of being random: statistical principles of iris recognition [J]. Pattern Recogn,2003,36:279-291.

[9] Wildes R P. Iris recognition: an emerging biometric technology[J]. Proceedings of the IEEE, 2002, 85(9): 1348-1363.

[10] Huang Y P,Luo S W,Chen E Y.An efficient iris recognition system[C]. In: International Conference on Machine Learning & Cybernetics.IEEE,2002:450-454.

[11] Liu Y,Yuan S,ZhuX,Cui Q.A practical iris acquisition system and a fast edgeslocating algorithm in iris recognition[C].In:20th lEEE Conference on Instrumentation and Measurement Technology, 2003: 166-168.

[12] Sung H,Lim J,Park J,Lee Y.Iris recognition using collarette boundary localization[C]. In:17th Interational Conference on Pattern Recognition,2004:857-860.

[13] Feng X,Fang C,DingX,WuY.Iris localization with dual coarse-to-finestrategyl[C].In:18th International Conference on Pattern Recognition,2006:553-55

[14] Nitin K. Mahadeo,Andrew P. Papliński,Sid Ray.Model-based pupil and iris localization[C].IEEE The 2012 International Joint Conference on Neural Networks (IJCNN),2012:1427-1433.

[15] Jayalakshmi S,Sundaresan M,IEEE.A survye on iris segmentation methods[M].New York:IEEE, 2013.

[16] Arts L P A, Van den Broek E L. The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis[J]. Nature Computational Science, 2022, 2(1): 47-58.