Palmprint recognition using eigenpalms features

By Guangming Lu, David Dapeng Zhang, Kuanquan Wang. Pattern Recognition Letters, 2003

557 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6a769a8a78097733d9d285a6c28b4b3c2ff767e2

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In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Palmprint identification using feature-level fusion (2006, methodology): “In addition to capture device, our group has also implemented various line-, texture-, and component-based approaches [7,9,10].”
A biometric identification system based on eigenpalm and eigenfinger features (2005, background): “For this reason,multimodal biometric systems —i.e., systems that integrate two or more different biometric characteristics—are being developed to provide an acceptable performance, to increase the reliability of decisions, and to increase robustness to fraudulent technologies [17].”
Ordinal palmprint represention for personal identification [represention read representation] (2005, background): “Various palmprint representations have been proposed for recognition, such as Line features [2], Feature points [3], Fourier spectrum [4], Eigenpalms features [5], Sobel’s and morphological features [6], Texture energy [7], Wavelet signatures [8], Gabor phase [9], Fusion code [10], Competitive code…”
An automated palmprint recognition system (2005, methodology): “Keywords: Biometric; Palmprint recognition; Palmprint pre-processing; Subspace projection methods; Similarity matching”
A survey of palmprint recognition (2009, methodology): “They use principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) [8, 12-13, 18, 42, 63, 66-67, 90].”
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, ”