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Related Concept Videos

Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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Prosopagnosia01:24

Prosopagnosia

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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
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Facial Feedback Hypothesis

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Related Experiment Video

Updated: May 24, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

Generating one biometric feature from another: faces from fingerprints.

Necla Ozkaya1, Seref Sagiroglu

  • 1Computer Engineering Department, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey. ss@gazi.edu.tr

Sensors (Basel, Switzerland)
|March 9, 2012
PubMed
Summary

This study introduces a novel artificial neural network system that generates complete facial images, including all features, solely from fingerprint data. This groundbreaking research demonstrates a strong, previously unrecognized correlation between fingerprints and facial biometrics.

Keywords:
Taguchiartificial neural networkbiometricsfacefingerprintintelligent system

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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

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Related Experiment Videos

Last Updated: May 24, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
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Published on: December 24, 2015

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

Area of Science:

  • Biometrics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Biometric systems typically rely on distinct features like fingerprints or faces.
  • Generating one biometric modality from another has been a significant challenge in the field.

Purpose of the Study:

  • To develop an artificial neural network (ANN) system capable of generating facial features from fingerprint data.
  • To establish and model the relationship between fingerprints and faces for biometric generation.
  • To create the first system that can generate all facial components from fingerprints alone.

Main Methods:

  • Utilized artificial neural networks (ANNs) for biometric feature generation.
  • Employed the Taguchi experimental design technique for system parameter optimization.
  • Evaluated system performance using 10-fold cross-validation with both qualitative and quantitative metrics.

Main Results:

  • Successfully generated complete facial images, including eyebrows, eyes, nose, mouth, ears, and face borders, from fingerprint data.
  • Demonstrated a strong correlation between fingerprint patterns and facial structures.
  • Achieved high accuracy and performance validated through rigorous testing methodologies.

Conclusions:

  • Confirmed the feasibility of generating one biometric feature (faces) from another (fingerprints).
  • Highlighted the significant underlying relationship between fingerprint and facial biometrics.
  • Paved the way for novel biometric fusion and generation techniques.