Biometric Identification through Hand Geometry Measurements

By R. Sánchez-Reillo, Carmen Sánchez Ávila, A. Gonzalez-Marcos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

553 citations recorded by the source corpus.

Subfields: Information Systems, Human-Computer Interaction, Signal Processing

View Paper: https://www.semanticscholar.org/paper/34b69aac4792741883a0bf7d46c92be283b47e20

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Citations in the corpus

A Physical Model of Human Skin and its Application for Search and Rescue (2012, background): “Hand and gesture recognition is important for designing new and improved human/computer interfaces [15, 54] and is currently based on the color and geometry of the hand [11,74].”
A review of biometric technology along with trends and prospects (2014, background): “Table 4: Summary of behavioral modalities: Modality Feature Set Recognition Techniques Datasets Voice Spectrum, Glottal pulse features, pitch, energy, duration, rhythm, temporal features, phones, idiolects, semantics, accent, pronunciation, LPCC [204], MFCC [205], VQ [206-208], HMM [209], GMM [210, ”
Online finger-knuckle-print verification for personal authentication (2010, background): “Fingerprint [1-3], palmprint [10-16], hand geometry [17-19], hand vein [20-21], and inner-knuckle-print [28-29] have been proposed and well investigated in the literature.”
Personal Verification Using Palmprint and Hand Geometry Biometric (2003, methodology): “Our image acquisition setup is inherently simple and does not employ any special illumination (as in [3]) nor does it use any pegs to cause any inconvenience to users (as in [20]).”
Continual Representation Learning for Biometric Identification (2020, background): “Biometric identification [23,51], including face recognition [11, 37, 67] and person re-identification (re-id) [34, 66, 71], has achieved significant progress in the recent years due to the advances in modern learnable representations [8, 9, 11, 20, 34, 52, 60, 67] and emerging large datasets [17, 2”
A biometric identification system based on eigenpalm and eigenfinger features (2005, methodology): “…systems are fingerprint [3], [4], hand geometry [5], [6], palm print [7], [8], [9], face [4], [10], iris [11], [12], retina [13], and ear [14]; the behavioral characteristics are signature [2], lip movement [15], speech [16], keystroke dynamics, gesture, andgait [1], [2].”
Shape-based hand recognition (2006, background): “For example, Sanchez-Reillo et al. [ 22 ] select 25 features, such as finger widths at different latitudes, finger and palm heights, finger deviations and the angles of the interfinger valleys with the horizontal, and model them with Gaussian mixtures.”
Competitive coding scheme for palmprint verification (2004, methodology): “The verification results of Competitive Code are comparable with the previous palmprint approaches and other hand-based biometric technologies, including hand geometry and fingerprint verification [2-7, 10].”
Personal Authentication Using Finger Knuckle Surface (2009, background): “The earlier approaches achieved the results by constraining the position and posture of hands by using pegs [2], [8], [11].”
Personal recognition using hand shape and texture (2006, unknown): “computed due to their poor reliability, as in [32], and its poor”
Biometrics: a tool for information security (2006, background): “Hand geometry recognition systems are based on a number of measurements taken from the human hand, including its shape, size of palm, and lengths and widths of the fingers [10].”
Palmprint identification using feature-level fusion (2006, unknown): “Keywords: Palmprint; Biometrics; Fusion; Security; Zero-crossing”
Online Palmprint Identification (2003, result): “This result is comparable with previous palmprint approaches and other hand-based biometric technologies including hand geometry and fingerprint verification [5], [6], [7], [11], [13], [14], [18], [19].”