1,534 research outputs found
Cancelable iris Biometrics based on data hiding schemes
The Cancelable Biometrics is a template protection scheme that can replace a stolen or lost biometric template. Instead of the original biometric template, Cancelable biometrics stores a modified version of the biometric template. In this paper, we have proposed a Cancelable biometrics scheme for Iris based on the Steganographic technique. This paper presents a non-invertible transformation function by combining Huffman Encoding and Discrete Cosine Transformation (DCT). The combination of Huffman Encoding and DCT is basically used in steganography to conceal a secret image in a cover image. This combination is considered as one of the powerful non-invertible transformation where it is not possible to extract the exact secret image from the Stego-image. Therefore, retrieving the exact original image from the Stego-image is nearly impossible. The proposed non-invertible transformation function embeds the Huffman encoded bit-stream of a secret image in the DCT coefficients of the iris texture to generate the transformed template. This novel method provides very high security as it is not possible to regenerate the original iris template from the transformed (stego) iris template. In this paper, we have also improved the segmentation and normalization process
Development of CUiris: A Dark-Skinned African Iris Dataset for Enhancement of Image Analysis and Robust Personal Recognition
Iris recognition algorithms, especially with the
emergence of large-scale iris-based identification systems, must
be tested for speed and accuracy and evaluated with a wide
range of templates – large size, long-range, visible and different
origins. This paper presents the acquisition of eye-iris images
of dark-skinned subjects in Africa, a predominant case of verydark-
brown iris images, under near-infrared illumination. The
peculiarity of these iris images is highlighted from the
histogram and normal probability distribution of their
grayscale image entropy (GiE) values, in comparison to Asian
and Caucasian iris images. The acquisition of eye-images for
the African iris dataset is ongoing and will be made publiclyavailable
as soon as it is sufficiently populated
Privacy-Preserving Facial Recognition Using Biometric-Capsules
Indiana University-Purdue University Indianapolis (IUPUI)In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design.
In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods
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