Periocular Region-Based Biometric Identification Using Local Binary Pattern and Its Variants

Background and purpose: Biometrics is a commonly studied research issue for both biomedical engineering and forensics sciences. Besides, the purpose of hidden biometrics is to discover hidden biometrics features. This work aims to demonstrate the biometric identification ability of coronary angiography images. Material and method: A new coronary angiography images database was collected to develop an automatic identification model. The used database was collected from 51 subjects and contains 2156 images. The developed model has to preprocess; feature generation using local binary pattern; feature selection with neighborhood component analysis; and classification phases. In the preprocessing phase; image rotations; median filter; Gaussian filter; and speckle noise addition functions have been used to generate filtered images. A multileveled extractor is presented using local binary pattern and maximum pooling together. The generated features are fed to neighborhood component analysis and the selected features are classified using k nearest neighbor classifier. Results: The presented angiography image identification method attained 99.86% classification accuracy on the collected database. Conclusions: The obtained findings demonstrate that the angiography images can be utilized as biometric identification. Moreover, we discover a new hidden biometric feature using coronary angiography images and name of this hidden biometric is coronary angiography print.

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Periocular region appearance cues for biometric identification

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops ◽

10.1109/cvprw.2010.5544621 ◽

2010 ◽

Cited By ~ 51

Author(s):

Damon L. Woodard ◽

Shrinivas J. Pundlik ◽

Jamie R. Lyle ◽

Philip E. Miller

Keyword(s):

Biometric Identification ◽

Periocular Region

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Penerapan Face Recognition pada Aplikasi Akademik Online

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) ◽

10.35870/jtik.v5i4.244 ◽

2021 ◽

Vol 5 (4) ◽

pp. 420

Author(s):

Budi Tri Utomo ◽

Iskandar Fitri ◽

Eri Mardiani

Keyword(s):

Face Recognition ◽

Local Binary Pattern ◽

Biometric Identification ◽

Grayscale Image ◽

Face Database ◽

Identification Process ◽

National University ◽

Haar Cascade ◽

Pattern Histogram ◽

Rgb Image

In the era of big data, the biometric identification process is growing very fast and is increasingly being implemented in many applications. Face recognition technology utilizes artificial intelligence (AI) to recognize faces that are already stored in the database. In this research, it is proposed to design an online academic login system at the National University using real time face recognition used OpenCV with the Local Binary Pattern Histogram algorithm and the Haar Cassade Classification method. The system will detect, recognize and compare faces with the stored face database. The image used is 480 x 680 pixels with a .jpg extension in the form of an RGB image which will be converted into a Grayscale image., to make it easier to calculate the histogram value of each face that will be recognized. With a modeling system like this it is hope to make it easy for user to log into online academics.Keywords:Face Recognition, Haar Cascade Clasifier, Local Binary Pattern Histogram, Online Akademic, OpenCV.

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