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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

937
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...
937

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In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol.

Wee How Khoh1, Ying Han Pang1, Hui Yen Yap1

  • 1Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, 75450, Malaysia.

F1000Research
|August 21, 2023
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Summary
This summary is machine-generated.

Researchers developed a new method for in-air hand gesture signature recognition, creating a publicly accessible database. This system achieves high accuracy for human classification and demonstrates robustness against various forgery attacks.

Keywords:
Dynamic SignatureForgeries AttackGesture RecognitionHand Gesture SignatureHand Gesture Signature DatabaseImage Processing

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Area of Science:

  • Computer Science
  • Biometrics
  • Human-Computer Interaction

Background:

  • Hand gesture recognition is advancing, with applications in human-computer interaction (HCI) for non-contact identification.
  • In-air hand gesture signature recognition enables user identification through unique hand movements.
  • A lack of publicly accessible databases and detailed protocols exists in this domain.

Purpose of the Study:

  • To demonstrate the procedure for collecting an in-air hand gesture signature database.
  • To provide a reference database for evaluation in the field of gesture recognition.
  • To establish a protocol for acquiring and processing in-air hand gesture signatures.

Main Methods:

  • Collected signatures from 100 volunteers across two sessions, generating genuine and forgery datasets.
  • Utilized a Microsoft Kinect sensor camera for signature acquisition.
  • Preprocessed data with hand localization and segmentation, followed by vector-based feature extraction.

Main Results:

  • Achieved 97.43% accuracy in classification using a multiclass Support Vector Machine (SVM).
  • Demonstrated low error rates in system robustness analysis: 2.41% for random forgery and 5.07% for skilled forgery.
  • Validated the effectiveness of extracted features for distinguishing genuine and forged signatures.

Conclusions:

  • In-air hand gesture signatures are feasible for reliable human classification.
  • The developed system exhibits robustness against both random and skilled forgery attacks.
  • The created database serves as a valuable resource for future research in biometric identification.