Personal Authentication Using Finger Knuckle Surface

By Ajay Kumar, Ch. Ravikanth. IEEE Transactions on Information Forensics and Security, 2009

299 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f2a5b0995f02e38204e38d20c0b6ee68b49c6a8e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Lei Zhang
2. David Dapeng Zhang
3. Almas Abbasi
4. Woo Chaw Seng
5. J. Unar
6. Lin Zhang
7. Hailong Zhu
8. Wangmeng Zuo

In Information Systems

1. Almas Abbasi
2. Woo Chaw Seng
3. J. Unar
4. Lin Zhang
5. Hailong Zhu
6. Lei Zhang
7. David Dapeng Zhang

In Signal Processing

1. Lei Zhang
2. Almas Abbasi
3. Woo Chaw Seng
4. J. Unar
5. David Dapeng Zhang
6. Lin Zhang
7. Hailong Zhu
8. Ajay Kumar

Citations in the corpus

• Online finger-knuckle-print verification for personal authentication (2010, methodology): “Unlike the systems in [22] and [25], which first capture the image of the whole hand and then extract the finger or finger knuckle surface areas, the proposed system captures the image around the finger knuckle area of a finger directly, which largely simplifies the following preprocessing steps.”
• A review of biometric technology along with trends and prospects (2014, methodology): “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, ”