Fisherpalms based palmprint recognition

By Xiangqian Wu, David Dapeng Zhang, Kuanquan Wang. Pattern Recognition Letters, 2003

445 citations recorded by the source corpus.

In this paper, a novel method for palmprint recognition, called Fisherpalms, is proposed. In this method, each pixel of a palmprint image is considered as a coordinate in a high-dimensional image space. A linear projection based on Fisher's linear discriminant is used to project palmprints from this high-dimensional original palmprint space to a significantly lower dimensional feature space (Fisherpalm space), in which the palmprints from the different palms can be discriminated much more efficiently. The relationship between the recognition accuracy and the resolution of the palmprint image is also investigated. The experimental results show that, in the proposed method, the palmprint images with resolution 32 × 32 are optimal for medium security biometric systems while those with resolution 64 × 64 are optimal for high security biometric systems. High accuracies (>99%) have been obtained by the proposed method and the speed of this method (responding time ≤ 0.4 s) is rapid enough for real-time palmprint recognition.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0402d42dc64d510297d88e63a01c9a9c7a0e1d53

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Citations in the corpus

An automated palmprint recognition system (2005, methodology): “where * denotes the convolution operator, Y2,1 ([2,1) subsampling along the rows (columns), H and G are a low pass and bandpass filter, respectively.”
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, ”
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].”
An Online System of Multispectral Palmprint Verification (2010, methodology): “IOMETRICS refers to the study of methods for recognizing humans based on one or more physical or behavioral traits [1, 2]. As an important biometrics characteristic, palmprint has been attracting much attention [ 3 ]‐[14] because it has merits, such as high speed, user friendliness, low cost, and hi”
Personal recognition using hand shape and texture (2006, methodology): “The palmprint matching using texture-based [7], line-based [10], and appearance-based methods [11] have been proposed literature.”
Palmprint verification based on robust line orientation code (2007, methodology): “Finally, a matching algorithm based on pixel-to-area comparison has been designed, which has better fault tolerant ability.”
Personal Authentication Using Finger Knuckle Surface (2009, background): “Hand-based biometrics has attracted lot of attention and personal identification by using palmprint [1], [2], [4]‐[ 6 ], [14], [15], [38]; hand geometry [7], [8]; 3-D finger geometry [9], [10]; and hand vein [11]‐[13] have been proposed in the literature.Theearlierapproachesachievedtheresultsby cons”
A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks (2008, background): “[ 22 ]‐[26]. For a typical palmprint recognitionsystembasedonaneuralnetwork,first,somesignificant features are extracted to reduce data dimension and computationalburden.Then,therecognition systemisperformedby neural networks.”