Pose-Invariant Hand Geometry for Human Identification Using Feature Weighted k-NN Classifier

Advances in Intelligent Systems and Computing - Information Technology and Applied Mathematics ◽

10.1007/978-981-10-7590-2_8 ◽

2018 ◽

pp. 115-129

Author(s):

Asish Bera ◽

Debotosh Bhattacharjee ◽

Mita Nasipuri

Keyword(s):

Human Identification ◽

Hand Geometry

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Biometric Technologies

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch062 ◽

2011 ◽

pp. 369-374

Author(s):

Yingzi ("Eliza") Du

Keyword(s):

Face Recognition ◽

Speech Signal ◽

Iris Recognition ◽

Voice Recognition ◽

Human Identification ◽

Emerging Technology ◽

Fingerprint Recognition ◽

Biological Traits ◽

Signal Characteristics ◽

Hand Geometry

Biometrics is an emerging technology for automatic human identification and verification using unique biological traits (Woodward, Orlans, & Higgins, 2002). These traits include face, fingerprints, iris, voice, hand geometry, handwriting, retina, and veins. For example, fingerprint recognition analyzes ridge ends, bifurcation, or dots of finger tips; voice recognition analyzes speech signal characteristics; iris recognition analyzes the pits, striations, filaments, rings, dark spots, and freckles of eyes; and face recognition analyzes facial parameters (Du et al., 2004). It is based on “something you are” rather than “something you have” (Du, 2005). Compared to the traditional identification and verification ways, such as user name/password, and paper IDs, biometrics is more convenient to use, reduces fraud, and is more secure (Reid, 2004).

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Biometric Human Identification of Hand Geometry Features Using Discrete Wavelet Transform

Discrete Wavelet Transforms - Biomedical Applications ◽

10.5772/19508 ◽

2011 ◽

Author(s):

Osslan Osiris Vergara Villegas ◽

Humberto de Jesus Ochoa Dominguez ◽

Vianey Guadalupe Cruz Sanchez ◽

Leticia Ortega ◽

Hiram Madero

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Human Identification ◽

Discrete Wavelet ◽

Hand Geometry

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Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Mobile Information Systems ◽

10.1155/2019/2325891 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Agata Giełczyk ◽

Michał Choraś ◽

Rafał Kozik

Keyword(s):

Feature Extraction ◽

Computational Complexity ◽

Real Life ◽

Human Identification ◽

Illumination Variation ◽

Novel Approach ◽

Biometric Systems ◽

Hand Geometry

Palmprint biometrics is a promising modality that enables efficient human identification, also in a mobile scenario. In this paper, a novel approach to feature extraction for palmprint verification is presented. The features are extracted from hand geometry and palmprint texture and fused. The use of a fusion of features facilitates obtaining a higher accuracy and, at the same time, provides more robustness to intrusive factors like illumination, variation, or noise. The major contribution of this paper is the proposition and evaluation of a lightweight verification schema for biometric systems that improves the accuracy without increasing computational complexity which is a necessary requirement in real-life scenarios.

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