Ear recognition using features inspired by visual cortex and support vector machine technique

Yaqubi, Mahboubeh; Faez, Karim; Motamed, Sara

doi:10.1109/iccce.2008.4580660

Cited by 25 publications

(18 citation statements)

References 6 publications

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“…This method investigates the generalization power of C2 features, inspired by the hierarchical organization of the primate visual ventral stream, over four important pattern recognition problems. Our visual system achieves high efficiency by means of neurons responsive to complex features which are themselves built upon simple orientation features in the visual hierarchy [2,3,11]. The main structure of the proposed method is shown at figure 4.…”

Section: Proposed Approachmentioning

confidence: 99%

“…In this reference, they combined these two techniques for the robust Ear verification problem. In addition to, demonstrated that this method is rotate and scale-invariant, and also, in experiment, it was found that, using of Gaussian filter in HMAX model in compared to using of Gabor filter, increases performance of ear recognition [2,3,11].…”

Section: Introductionmentioning

confidence: 99%

“…So that image denoising is one of the important preprocessing of verification systems. Performance of feature extraction methods and verification systems depend on quality of input image [10,11].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

Tavassoli¹,

Yaqubi²

2011

IJCA

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Today, Biometric systems are considered superior in technological developments, because they provide a nontransferable means of identifying people not just cards or badges. The image enhancement step is designed to reduce noise in this area. The key point about an identification method that is "nontransferable" means it cannot be given or lent to another individual so nobody can get around the system they personally have to go through the control point. The image enhancement before feature extraction system can be very efficient. In this paper a new method is proposed to raise the performance of an ear verification system, since at first, using hybrid denoising method, the noises removed from ear image and then the next step denoisy image is used for verification system. Experimental results in this study show that Gaussian noises well removed from the ear images and has acceptable affect on verification accuracy.

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Section: Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

Tavassoli¹,

Yaqubi²

2011

IJCA

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“…Yaqubi et al use features obtained by a combination of position and scale-tolerant edge detectors over multiple positions and orientations of the image [74]. This feature extraction method is called HMAX model and is inspired by the visual cortex of primates and combines simple features to more complex semantic entities.…”

Section: Classifiers and Statistical Approachesmentioning

confidence: 99%

Ear biometrics: a survey of detection, feature extraction and recognition methods

2012

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The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features.The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people.Because of these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, we categorize and summarize approaches to ear detection and recognition in 2D and 3D images. Then, we provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which we consider to be the most important application of ear recognition characteristic in the near future.

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“…Hmax model is motivated by a quantitative model of visual cortex, and SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. This method (Yaqubi et al, 2008) combines these two techniques for the robust Ear recognition problem. With Hmax, a new set of features has been introduced for human identification, each element of this set is a complex feature obtained by combining position-and scale-tolerant edge detectors over neighboring positions and multiple orientations.…”

Section: Related Workmentioning

confidence: 99%

Gabor-Based RCM Features for Ear Recognition

Yazdanpanah¹,

Faez²

2011

State of the Art in Biometrics

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Ear recognition using features inspired by visual cortex and support vector machine technique

Cited by 25 publications

References 6 publications

An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

Ear biometrics: a survey of detection, feature extraction and recognition methods

Gabor-Based RCM Features for Ear Recognition

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