Authentication gets personal with biometrics

Ortega‐Garcia, Javier; Bigün, Josef; Reynolds, Douglas A.; González-Rodríguez, Joaquín

doi:10.1109/msp.2004.1276113

Cited by 118 publications

(59 citation statements)

References 40 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Examples include voice recognition, keystroke recognition (distinctive rhythms in the timing between keystrokes for certain pairs of characters), signature recognition (handwriting or character shapes, timing and pressure of the signature process). Gait recognition or the pattern of walking or locomotion is also used as a biometric measure (Ortega-Garcia et al, 2004).…”

Section: Biometric Techniquesmentioning

confidence: 99%

Haptics and the Biometric Authentication Challenge

Kanneh¹,

Sakr²

2010

Advances in Haptics

View full text Add to dashboard Cite

Section: Biometric Techniquesmentioning

confidence: 99%

Haptics and the Biometric Authentication Challenge

Kanneh¹,

Sakr²

2010

Advances in Haptics

View full text Add to dashboard Cite

“…The development of automatic face recognition (AFR) has attracted significant research attention due to increasing demand on its applications. Since AFR is considered to be a natural, non-intimidating, and widely accepted biometric identification method [1], [2], it has the potential of becoming the leading biometric technology. Unfortunately, it is also one of the most difficult pattern recognition problems.…”

Section: Introductionmentioning

confidence: 99%

Invariant Illumination Face Recognition Using Optimized PNN Topology through Neuron Orthogonalization and Genetic Algorithm

Kusumoputro¹,

Lina²

2014

IJCCE

View full text Add to dashboard Cite

Abstract-The development of face recognition system attracted significant research attention due to increasing demand on its applications. However, the process of converge to a conclusion of a known-face based from a 2D incoming face images is very difficult, especially when large illumination variations are present in the input space. In this paper, we implemented the illumination compensation praprocessing system in conjuction with the optimized-Probabilistic Neural Networks as a classifier. PNN has shown marvelous higher recognition capability, however, determining the best tuning parameter is very difficult. Neural topology is firstly determined by looking for the most representative neuron using Orthogonal Algorithm, followed by evolutionary determine the best smoothing parameter through Genetic Algorithm. Experiments are conducted using face images under various illumination conditions, and results are presented which illustrate the potential of this approach.

show abstract

“…Recognizing people by biometrics (such as fingerprints, faces, speech and iris patterns) has applications in surveillance, forensics, transaction authentication, and various forms of access control, such as border checkpoints and access to digital information [13], [15], [22].…”

Section: Introductionmentioning

confidence: 99%

“…The use of the face as a biometric is particularly attractive, as it can involve little or no interaction with the person to be verified [15]. Various techniques have been proposed for face classification; some examples are systems based on Principal Component Analysis (PCA) feature extraction [24], modular PCA [16], Elastic Graph Matching (EGM) [6], and Support Vector Machines [19].…”

Section: Introductionmentioning

confidence: 99%