PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris

Current segmentation methods have limitations for multi-source heterogeneous iris segmentation since differences of acquisition devices and acquisition environment conditions lead to images of greatly varying quality from different iris datasets. Thus, different segmentation algorithms are generally applied to distinct datasets. Meanwhile, deep-learning-based iris segmentation models occupy more space and take a long time. Therefore, a lightweight, precise, and fast segmentation network model, PFSegIris, aimed at the multi-source heterogeneous iris is proposed by us. First, the iris feature extraction modules designed were used to fully extract heterogeneous iris feature information, reducing the number of parameters, computation, and the loss of information. Then, an efficient parallel attention mechanism was introduced only once between the encoder and the decoder to capture semantic information, suppress noise interference, and enhance the discriminability of iris region pixels. Finally, we added a skip connection from low-level features to catch more detailed information. Experiments on four near-infrared datasets and three visible datasets show that the segmentation precision is better than that of existing algorithms, and the number of parameters and storage space are only 1.86 M and 0.007 GB, respectively. The average prediction time is less than 0.10 s. The proposed algorithm can segment multi-source heterogeneous iris images more precisely and quicker than other algorithms.

Generative Information Hiding of Iris Feature Data Based on Gaussian Fuzzy Algorithm

The existing methods used for hiding the iris feature data were time-consuming for iris feature extraction. Meanwhile, the information security after hiding was also low, leading to low efficiency and security of information hiding. Therefore, a method of hiding iris features data generative information based on a Gaussian fuzzy algorithm was proposed. In the preprocessing stage of the image, the weighted average method was adopted for the gray-level transformation of the iris image, and the Gaussian fuzzy algorithm was used to smooth the image. In addition, the Laplacian convolution kernel was used to sharpen the image. The iris regions were normalized. The iris feature data was extracted by employing 2D Gabor wavelet. Moreover, the iris feature data was encrypted and decrypted using the AES algorithm, and hence, effectively enhancing the security of the generative information of iris feature data. Experimental results show that the proposed method can extract iris feature information within ten seconds, and the data security coefficient is high thus the proposed method efficiently realizes the information hiding.

A Brief Survey on Modern Iris Feature Extraction Methods

يتم استخدام انظمة المقاييس الحيوية للتحقق من الشخص بناءً على الخصائص الخاصة للشخص والتي استخدمتها في تطبيقات واسعة مثل الاتصالات الآمنة والتجارة حيث تتطلب مصادقة هوية الشخص. يستخدم نظام التعرف على الاشخاص بأستخدام قزحية العين على نطاق واسع لاستقراره وتفرده مقارنة بأنظمة المقاييس الحيوية الأخرى. يتكون نظام التعرف القائم على القزحية من مراحل هي توطين القزحية وتطبيعها واستخراج المميزات والمطابقة. تأثير استخراج المميزات كبير على دقة وموثوقية نظام القياسات الحيوية. في هذا العمل ، تم إجراء مسح لبعض أحدث الأعمال البحثية ومقارنتها من حيث الدقة في تميز الافراد.

Iris Image Segmentation and Localization using Dynamic Reconfigurable Processor (DRP)

Recognition of the Iris is among the finest techniques in the field of bio-metrics identification, because the iris has characteristics that are unique and stay the same all through the individual’s life. Iris recognition phases are namely image acquisition, segmentation of iris, localization of iris, feature extraction of iris and matching. This paper, which is an extension of the survey paper Smaran et.al[1], concentrates purely on the procedures of image capture, segmentation as well as localization of the iris. The aim of the paper is to optimize the above mentioned processes in terms of distance of capturing the image, time taken for memory and computation requirements, using the DRP (Dynamic Re-Configurable Processor) technology, uniquely developed by Renesas Electronics (www.renesas.com).