Entropy-cum-Hough-transform-based ear detection using ellipsoid particle swarm optimization

Prajwal, Chidananda,; Srinivas, P. S.; Manikantan, K.; Ramachandran, Shanmugalakshmi

doi:10.1007/s00138-015-0669-y

Cited by 20 publications

(11 citation statements)

References 35 publications

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“…Chidananda et al have proposed an entropy‐cum‐Hough‐transform‐based approach to enhance the performance of an ear detection under varying conditions of pose and background in facial images. They have used a unique amalgamation of ellipsoid ear classifier (EEC) and ear localizer (HEL).…”

Section: Related Workmentioning

confidence: 99%

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Unconstrained ear detection using ensemble‐based convolutional neural network model

Ganapathi

Prakash

Dave

et al. 2019

Concurrency and Computation

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This paper presents a technique for ear detection from 2D profile face images that is capable of significantly reducing the false positives. In an ear biometrics system, recognition performance highly depends on the performance of the ear detection module. The trade-off between the complexity and the false positive detection is one of the essential component, where the complexity of a system increases proportionally to achieve a zero false positive rate detection.In literature, available ear detection techniques based on handcrafted features face challenges with low-quality acquired images affected by illumination, occlusion, and pose variations. We propose an ear detection technique using ensemble of convolutional neural network (CNN). The first part of the technique trains three models of CNN on a given dataset, whereas in later part, weighted average of the outputs of trained models is utilized to detect the ear regions. The used ensemble models show better performance as compared to the case when each individual model is used standalone. The proposed technique is being evaluated on two databases, viz, IIT Indore-Collection A (IIT-Col A) database and annotated web ear (AWE) database. Experimental results of ear detection demonstrate the superior performance of the proposed technique over other state-of-the-art techniques in handling illumination, occlusion, and pose variations. KEYWORDS deep learning, ear detection, ensemble model, unconstrained environment INTRODUCTIONIn recent years, ear biometrics has been considered a reliable biometrics and has got attention over face mainly due to its invariant behavior to the age of a person. Biometrics systems based on face have to consider the changes occurring in the face with a change in age or expression.The ear of a person remains consistent in its shape and it is fixed in its position at the center of the profile image. To obtain reliable performance, face images are required to be captured in the controlled setting, while in case of ear, it can be captured with a foreseeable background due to its fixed side face background. An ear biometric system mainly consists of two processes, ie, Ear detection and Ear recognition. Majority of the ear biometrics techniques in the literature use manually cropped ear images to perform ear recognition process. Furthermore, the ear biometric techniques that crops ear automatically are able to detect ear only when the ear has small variations in the side face image. In cases of pose or scale variations, these techniques are found to be inefficient. Additionally, in a real-time environment, these techniques lack in being fully automatic approaches and speed.Performance of an ear detection technique based on handcrafted feature learning methods depends on the dataset used for learning the features. 1 These methods are prone to produce erroneous results when used with the images captured in unconstrained settings like different illumination, background, and pose. Tasks like extraction of suitable features along with the discriminati...

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Section: Related Workmentioning

confidence: 99%

“…These techniques may fail if the pre‐processing step is not carried out successfully. Moreover, the other techniques depend on hand‐crafted features and work only in a controlled illumination and single background. There are few deep learning‐based techniques for ear localization.…”

Section: Comparison With State‐of‐the‐art Techniquesmentioning

confidence: 99%

Unconstrained ear detection using ensemble‐based convolutional neural network model

Ganapathi

Prakash

Dave

et al. 2019

Concurrency and Computation

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“…The authors of [33] propose an ear detection approach that relies on the entropy‐Hough transform. A combination of a hybrid ear localiser and an ellipsoid ear classifier is used to predict locations of ears.…”

Section: Related Workmentioning

confidence: 99%

Convolutional encoder–decoder networks for pixel‐wise ear detection and segmentation

2018

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Object detection and segmentation represents the basis for many tasks in computer and machine vision. In biometric recognition systems the detection of the region-of-interest (ROI) is one of the most crucial steps in the processing pipeline, significantly impacting the performance of the entire recognition system. Existing approaches to ear detection, are commonly susceptible to the presence of severe occlusions, ear accessories or variable illumination conditions and often deteriorate in their performance if applied on ear images captured in unconstrained settings. To address these shortcomings, we present a novel ear detection technique based on convolutional encoder-decoder networks (CEDs). We formulate the problem of ear detection as a two-class segmentation problem and design and train a CED-network architecture to distinguish between imagepixels belonging to the ear and the non-ear class. Unlike competing techniques, our approach does not simply return a bounding box around the detected ear, but provides detailed, pixel-wise information about the location of the ears in the image. Experiments on a dataset gathered from the web (a.k.a. in the wild) show that the proposed technique ensures good detection results in the presence of various covariate factors and significantly outperforms competing methods from the literature.

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“…The segmentation accuracy rate obtained after analysing the method and proving results is 64.2%. Chidananda et al (2015) developed automated ear detection in facial images method to localise ear image under challenging situations such as varying backgrounds, occlusion and various postures. This study proposed an integration of entropy texture analysis filter and Hough transform to improve and get accurate detection of ear image.…”

Section: Previous Work and Motivationmentioning

confidence: 99%

Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background

Thabet

Khalid

Sulaiman

et al. 2017

J Ambient Intell Human Comput

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Entropy-cum-Hough-transform-based ear detection using ellipsoid particle swarm optimization

Cited by 20 publications

References 35 publications

Unconstrained ear detection using ensemble‐based convolutional neural network model

Unconstrained ear detection using ensemble‐based convolutional neural network model

Convolutional encoder–decoder networks for pixel‐wise ear detection and segmentation

Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background

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