Hand Recognition Using Implicit Polynomials and Geometric Features

Öden, Cenker; Erçil, Aytül; Yildiz, Vedat Taylan; Kirmizitas, Hikmet; Büke, Burak

doi:10.1007/3-540-45344-x_49

Cited by 20 publications

(14 citation statements)

References 7 publications

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“…Some of the other works do not use any pegs to cause any inconvenience to the users. In theses systems some kind of image alignments is required prior to feature extraction [3], [4], [12].…”

Section: Introductionmentioning

confidence: 99%

“…Another possibility for user recognition based on hand image is to use the geometry of a person's hand to authenticate his identity [1][2][3][4][5][6][7][8][9][10][11][12]. Features like area/size of palm, length, width, and angle of fingers, palms aspect ratio, and various contour transformations can be used in such a system.…”

Section: Introductionmentioning

confidence: 99%

“…Hand model can also be constructed from a set of images of the hand from different views [1]. In [3], [4] implicit polynomials, which are very powerful in object modeling, have been used for recognizing hand shapes.…”

Section: Introductionmentioning

confidence: 99%

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A new ensemble based classifier using feature transformation for hand recognition

Jafarzadegan

Mirzaei

2008

2008 Conference on Human System Interactions

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This paper presents a new method for user identification based on hand images. Because in user identification the processing time is an important issue, we use only the hand boundary as a hand representation. Using this representation, an alignment technique is used to make the index of corresponding features of different hands get the same index in the feature vector. To improve the classification performance a new ensemble-based method is proposed. This method uses feature transformation to create the needed diversity between base classifiers. In other words, first different sets of features are created by transforming the original features into new spaces where the samples are well separated, and then each base classifier is trained on one of these newly created features sets. The proposed method for constructing an ensemble of classifiers is a general method which may be used in any classification problem. The results of experiments performed to assess the presented method and compare its performance with other alternative classification methods are encouraging.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new ensemble based classifier using feature transformation for hand recognition

Jafarzadegan

Mirzaei

2008

2008 Conference on Human System Interactions

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“…al. [8], who developed a system for identification and verification applications using implicit polynomials. Wong and Shi [10] proposed a feature-based framework for hand geometry recognition, based upon matching of geometrical and shape features.…”

Section: Introductionmentioning

confidence: 99%

Biometric Hand Recognition Using Neural Networks

Martínez

Orrite

Herrero

2005

Computational Intelligence and Bioinspired Systems

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Abstract.A new approach for personal identification using hand geometry based upon geometrical and shape features is presented. We propose a new pegless hand geometry verification system where the users are free to put their hand in arbitrary fashion. A Linear Discirminant Analysis if applied to the raw data in order to perform a best clustering of the feature space. The combination of three different neural network classifiers (unsupervised SOM, supervised SOM and LVQ) gives 0.35% FAR and 0.15% FRR. The method has been tested on a large size database of 1400 images for training and 1400 for test from 280 individuals suitable for medium and low security applications.

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“…The authors of [9] perform initial normalization of hand contour and apply independent component analysis or principal component analysis to extract its features for recognition. In [10] finger contours are modeled using implicit polynomials, and feature vector is composed of algebraic invariants from polynomial coefficients. Hand shapes can also be described by Zernike moments [11] that are invariant to translation and scale.…”

Section: Introductionmentioning

confidence: 99%

Hand Shape Recognition from Natural Hand Position

Bakina

Mestetskiy

2011

2011 International Conference on Hand-Based Biometrics

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The article proposes a new approach to hand shape recognition that allows comparison of hands with separated fingers as well as with touching fingers. The idea is to construct a flexible hand model from the set of hand images in different poses. This model allows us to simulate different hand transformations (for example, rotations of fingers) and analyze hand shape after them. Person recognition is performed by matching the reference model to a test hand image. The proposed similarity measure is normalized symmetric difference between superposed silhouettes of test hand and transformed reference hand. The article also introduces a prototype of peg-free person recognition system based on the proposed approach and experimental results on real data.

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Hand Recognition Using Implicit Polynomials and Geometric Features

Cited by 20 publications

References 7 publications

A new ensemble based classifier using feature transformation for hand recognition

A new ensemble based classifier using feature transformation for hand recognition

Biometric Hand Recognition Using Neural Networks

Hand Shape Recognition from Natural Hand Position

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