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Convolutional encoder-decoder networks for pixel-wise ear detection and segmentation
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- Emersic, Ziga (author)
- Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, SL-1000 Ljubljana, Slovenia.
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- Gabriel, Luka L. (author)
- KTH
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- Struc, Vitomir (author)
- Univ Ljubljana, Fac Elect Engn, Trzaska 25, SL-1000 Ljubljana, Slovenia.
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- KTH Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, SL-1000 Ljubljana, Slovenia (creator_code:org_t)
- 2018-03-14
- 2018
- English.
- In: IET Biometrics. - : INST ENGINEERING TECHNOLOGY-IET. - 2047-4938 .- 2047-4946. ; 7:3, s. 175-184
- Journal article (peer-reviewed)
Abstract
Subject headings
Close
- 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 image-pixels 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- object detection
- computer vision
- biometrics (access control)
- feature extraction
- image segmentation
- ear
- convolutional encoder-decoder
- pixel-wise ear detection
- machine vision
- biometric recognition systems
- entire recognition system
- ear accessories
- ear images
- ear detection technique
- two-class segmentation problem
- design
- image-pixels
- nonear class
- detected ear
- pixel-wise information
- good detection results
Publication and Content Type
- ref (subject category)
- art (subject category)
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- Emersic, Ziga
- Gabriel, Luka L.
- Struc, Vitomir
- Peer, Peter
- About the subject
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- NATURAL SCIENCES
- NATURAL SCIENCES
- and Computer and Inf ...
- Articles in the publication
- IET Biometrics
- By the university
- Royal Institute of Technology
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