A new force field transform for ear and face recognition

Hurley, David J.; Nixon, Mark S.; Carter, J.N.

doi:10.1109/icip.2000.900883

Cited by 36 publications

(21 citation statements)

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“…They had used small potential energy wells as ear features which are basically clustering or converging points of force field lines and suggest local energy peaks in the scalar potential energy surface (Hurley et al, 2002a(Hurley et al, , 2002b(Hurley et al, , 2000. They had demonstrated that the potential energy matrix is invertible and, therefore, the original image can be fully recovered from the potential energy surface.…”

Section: Force Field Feature Extraction Techniquementioning

confidence: 97%

“…The transform ultimately replaces intensity of each pixel by that force field. This force field is a vector quantity so pixels having identical neighbors will be represented by null (Hurley et al, 2002a(Hurley et al, , 2002b(Hurley et al, , 2000(Hurley et al, , 2005Banerjee and Chatterjee, 2015). So the effect of an approximately smooth background around any ear sample can be significantly eliminated by applying aforesaid transform.…”

Section: Introductionmentioning

confidence: 97%

“…In this paper, we apply a recently developed feature extraction scheme that is force field transform for this purpose. Force field transform was originally proposed by Hurley et al (2002a, Contents 2002b, 2000, 2005) and was primarily developed for the purpose of ear feature extraction where a force field is created around each pixel with the collective effort of surrounding pixels (Hurley et al, 2002a(Hurley et al, , 2002b(Hurley et al, , 2005(Hurley et al, , 2000Banerjee and Chatterjee, 2015). The transform ultimately replaces intensity of each pixel by that force field.…”

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Image set based ear recognition using novel dictionary learning and classification scheme

Banerjee

Chatterjee

2016

Engineering Applications of Artificial Intelligence

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Section: Force Field Feature Extraction Techniquementioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Image set based ear recognition using novel dictionary learning and classification scheme

Banerjee

Chatterjee

2016

Engineering Applications of Artificial Intelligence

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“…The question of transform invertibility is considered as this establishes that the transforms are information preserving. Further details of invariance, including initialization invariance, scale invariance, and noise tolerance can be found in [10][11][12][13].…”

Section: Force Field Transformsmentioning

confidence: 99%

Force Field Feature Extraction for Ear Biometrics

Hurley¹

2007

Series in Machine Perception and Artificial Intelligence

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The overall objective in defining feature space is to reduce the dimensionality of the original pattern space, whilst maintaining discriminatory power for classification. To meet this objective in the context of ear biometrics a new force field transformation treats the image as an array of mutually attracting particles that act as the source of a Gaussian force field. Underlying the force field there is a scalar potential energy field, which in the case of an ear takes the form of a smooth surface that resembles a small mountain with a number of peaks joined by ridges. The peaks correspond to potential energy wells and to extend the analogy the ridges correspond to potential energy channels. Since the transform also turns out to be invertible, and since the surface is otherwise smooth, information theory suggests that much of the information is transferred to these features, thus confirming their efficacy. We previously described how field line feature extraction, using an algorithm similar to gradient descent, exploits the directional properties of the force field to automatically locate these channels and wells, which then form the basis of characteristic ear features. We now show how an analysis of the mechanism of this algorithmic approach leads to a closed analytical description based on the divergence of force direction, which reveals that channels and wells are really manifestations of the same phenomenon. We further show that this new operator, with its own distinct advantages, has a striking similarity to the Marr-Hildreth operator, but with the important difference that it is non-linear. As well as addressing faster implementation, invertibility, and brightness sensitivity, the technique is also validated by performing recognition on a database of ears selected from the XM2VTS face database, and by comparing the results with the more established technique of Principal Components Analysis. This confirms not only that ears do indeed

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“…The best result, obtained by the fusion of both systems, was a rank-one accuracy of 73.65%. Juefei-Xu et al [18], [19] fused LBP and SIFT with other local and global feature extractors including Walsh masks [20], Laws Masks [21], DCT [22], DWT [23], Force Fields [24], SURF [25], Gabor filters [26], and Laplacian of Gaussian. The best result obtained was a rank-one accuracy of 53.2% by fusion of DWT and LBP.…”

Section: Introductionmentioning

confidence: 99%

Periocular Recognition by Detection of Local Symmetry Patterns

Mikaelyan

Alonso‐Fernandez

Bigün

2014

2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems

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Abstract-We present a new system for biometric recognition using periocular images. The feature extraction method employed describes neighborhoods around keypoints by projection onto harmonic functions which estimates the presence of a series of various symmetric curve families around such keypoints. The iso-curves of such functions are highly symmetric w.r.t. the keypoints and the estimated coefficients have well defined geometric interpretations. The descriptors used are referred to as Symmetry Assessment by Feature Expansion (SAFE). Extraction is done across a set of discrete points of the image, uniformly distributed in a rectangular-shaped grid positioned in the eye center. Experiments are done with two databases of iris data, one acquired with a close-up iris camera, and another in visible light with a webcam. The two databases have been annotated manually, meaning that the radius and center of the pupil and sclera circles are available, which are used as input for the experiments. Results show that this new system has a performance comparable with other periocular recognition approaches. We particularly carry out comparative experiments with another periocular system based on Gabor features extracted from the same set of grid points, with the fusion of the two systems resulting in an improved performance. We also evaluate an iris texture matcher, providing fusion results with the periocular systems as well.

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A new force field transform for ear and face recognition

Cited by 36 publications

References 1 publication

Image set based ear recognition using novel dictionary learning and classification scheme

Image set based ear recognition using novel dictionary learning and classification scheme

Force Field Feature Extraction for Ear Biometrics

Periocular Recognition by Detection of Local Symmetry Patterns

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