3,584 research outputs found
A novel image enhancement method for palm vein images
Palm vein images usually suffer from low contrast due to skin surface scattering the radiance of NIR light and image sensor limitations, hence require employing various techniques to enhance the contrast of the image prior to feature extraction. This paper presents a novel image enhancement method referred to as Multiple Overlapping Tiles (MOT) which adaptively stretches the local contrast of palm vein images using multiple layers of overlapping image tiles. The experiments conducted on the CASIA palm vein image dataset demonstrate that the MOT method retains the finer subspace details of vein images which allows excellent feature detection and matching with SIFT and RootSIFT features. Results on existing palm vein recognition systems demonstrate that the proposed MOT method delivers lower EER values outperforming other existing palm vein image enhancement methods
Multispectral Palmprint Encoding and Recognition
Palmprints are emerging as a new entity in multi-modal biometrics for human
identification and verification. Multispectral palmprint images captured in the
visible and infrared spectrum not only contain the wrinkles and ridge structure
of a palm, but also the underlying pattern of veins; making them a highly
discriminating biometric identifier. In this paper, we propose a feature
encoding scheme for robust and highly accurate representation and matching of
multispectral palmprints. To facilitate compact storage of the feature, we
design a binary hash table structure that allows for efficient matching in
large databases. Comprehensive experiments for both identification and
verification scenarios are performed on two public datasets -- one captured
with a contact-based sensor (PolyU dataset), and the other with a contact-free
sensor (CASIA dataset). Recognition results in various experimental setups show
that the proposed method consistently outperforms existing state-of-the-art
methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA)
are the lowest reported in literature on both dataset and clearly indicate the
viability of palmprint as a reliable and promising biometric. All source codes
are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z.
Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral
Palmprint Encoding for Human Recognition", International Conference on
Computer Vision, 2011. MATLAB Code available:
https://sites.google.com/site/zohaibnet/Home/code
Sistem Identifikasi Palm Vein melalui Proses Registrasi Berbasis Korelasi
Sistem biometrik merupakan sistem identifikasi yang memanfaatkan fitur atau ciri unik yang secara natural ada pada manusia yang dapat digunakan sebagai sumber data (modalitas) seperti retina, gait, sidik jari, pembuluh darah, dsb. Saat ini, palm vein merupakan modalitas yang sering dikaji dalam penelitian, karena palm vein memiliki kelebihan unik yaitu pembuluh darah halus pada bagian bawah telapak tangan yang sulit untuk dipalsukan. Masalah yang sering ditemui adalah saat pengambilan data terdapat variansi yang dipengaruhi oleh beberapa faktor seperti, modul sensor, pencahayaan, dan posisi modalitas. Dalam tugas akhir ini penulis menerapkan image registration / alignment yang bertujuan mendapatkan data yang lebih konsisten, sehingga dapat meningkatkan performansi sistem. Image registration yang digunakan adalah Normalized Cross-Correlation (NCC) untuk membantu mengukur informasi yang sama antara citra β citra yang sudah diambil. Selanjutnya, proses ekstraksi ciri menggunakan Local Binary Pattern (LBP) pada palm vein kemudian dilakukan proses matching. Sistem ini menggunakan tiga dataset yaitu PUTVEIN ukuran100%, PUTVEIN ukuran50%, dan PUTVEIN ukuran25%. Dengan menerapkan LBP dan NCC, sistem mampu meningkatkan akurasi hingga 93.83% pada Dataset PUTVEIN ukuran25% dengan rasio data model dan uji sebesar 6 : 6.
Kata kunci : biometrik, palm vein, image registration / alignment, Normalized Cross-Correlation, Local Binary Patter
An Efficient Vein Pattern-based Recognition System
This paper presents an efficient human recognition system based on vein
pattern from the palma dorsa. A new absorption based technique has been
proposed to collect good quality images with the help of a low cost camera and
light source. The system automatically detects the region of interest from the
image and does the necessary preprocessing to extract features. A Euclidean
Distance based matching technique has been used for making the decision. It has
been tested on a data set of 1750 image samples collected from 341 individuals.
The accuracy of the verification system is found to be 99.26% with false
rejection rate (FRR) of 0.03%.Comment: IEEE Publication format, International Journal of Computer Science
and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features
Designing an end-to-end deep learning network to match the biometric features
with limited training samples is an extremely challenging task. To address this
problem, we propose a new way to design an end-to-end deep CNN framework i.e.,
PVSNet that works in two major steps: first, an encoder-decoder network is used
to learn generative domain-specific features followed by a Siamese network in
which convolutional layers are pre-trained in an unsupervised fashion as an
autoencoder. The proposed model is trained via triplet loss function that is
adjusted for learning feature embeddings in a way that minimizes the distance
between embedding-pairs from the same subject and maximizes the distance with
those from different subjects, with a margin. In particular, a triplet Siamese
matching network using an adaptive margin based hard negative mining has been
suggested. The hyper-parameters associated with the training strategy, like the
adaptive margin, have been tuned to make the learning more effective on
biometric datasets. In extensive experimentation, the proposed network
outperforms most of the existing deep learning solutions on three type of
typical vein datasets which clearly demonstrates the effectiveness of our
proposed method.Comment: Accepted in 5th IEEE International Conference on Identity, Security
and Behavior Analysis (ISBA), 2019, Hyderabad, Indi
Future of Human Security Based on Computational Intellegence
This paper discusses the contact less palm veinauthentication device that uses blood vessel patterns as a personal identifying factor. The vein information is hard to duplicate since veins are internal to the human Body. This paper presents a review on the palm vein authentication process and its relevance and competence as compared to the contemporary Biometric methods. Th is authentication technology offers a high level of Accuracy. The importance of biometrics in the current field of Security has been illustrated in this paper. We have also outlined opinions about the utility of biometric authentication systems, comparison between different techniques and their advantages and disadvantage. Its significance is studied in this paper with reference to the banks, E-Voting, point of sale outlets and card/document less security system. Fujitsu plans to further expand applications for this technology by downsizing the sensor and improving the certification speed. I.2.m C.2.
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