Ontology type: schema:Chapter
2014
AUTHORS ABSTRACTBeing an emerging biometric characteristic, automated ear recognition is making its way into forensic image analysis for law enforcement in the last decades. One of the most important challenges for this application is to deal with loosely constrained acquisition scenarios and large databases of reference samples. The research community has come up with a variety of feature extraction methods that are capable of handling occlusions and blur. However, these methods require the images to be geometrically normalized, which is mostly done manually at the moment. In this work, we propose a segmentation and normalization method for ear images that is using cascaded pose regression (CPR). We show that CPR returns accurate rotation and scale estimates, even for full profile images, where the ear has not been segmented yet. We show that the segmentation accuracy of CPR outperforms state of the art detection methods and that CPR improves the recognition rate of an ear recognition system that uses state of the art appearance features. More... »
PAGES261-272
Secure IT Systems
ISBN
978-3-319-11598-6
978-3-319-11599-3
http://scigraph.springernature.com/pub.10.1007/978-3-319-11599-3_16
DOIhttp://dx.doi.org/10.1007/978-3-319-11599-3_16
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1010559835
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"name": [
"University of Applied Sciences Darmstadt - CASED, Haardtring 100, 64295\u00a0Darmstadt, Germany"
],
"type": "Organization"
},
"familyName": "Pflug",
"givenName": "Anika",
"id": "sg:person.07433647331.65",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07433647331.65"
],
"type": "Person"
},
{
"affiliation": {
"name": [
"University of Applied Sciences Darmstadt - CASED, Haardtring 100, 64295\u00a0Darmstadt, Germany"
],
"type": "Organization"
},
"familyName": "Busch",
"givenName": "Christoph",
"id": "sg:person.011143356603.69",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011143356603.69"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/978-3-642-04070-2_5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027468771",
"https://doi.org/10.1007/978-3-642-04070-2_5"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s1355-0306(01)71885-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039073519"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bfb0020217",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052658614",
"https://doi.org/10.1007/bfb0020217"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1049/iet-bmt.2011.0003",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1056818613"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tpami.2002.1017623",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061742396"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tpami.2009.23",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061743773"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.14257/ijsip.2013.6.5.05",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1067240553"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.14257/ijsip.2013.6.5.05",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1067240553"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/cvpr.2005.177",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093997066"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/iccv.2013.243",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094024488"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/ccst.2012.6393543",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094517254"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/iccv.2013.191",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094564562"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/cvpr.2010.5540094",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1095128699"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icpr.2008.4761847",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1095608368"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/ccst.2012.6393542",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1095718416"
],
"type": "CreativeWork"
}
],
"datePublished": "2014",
"datePublishedReg": "2014-01-01",
"description": "Being an emerging biometric characteristic, automated ear recognition is making its way into forensic image analysis for law enforcement in the last decades. One of the most important challenges for this application is to deal with loosely constrained acquisition scenarios and large databases of reference samples. The research community has come up with a variety of feature extraction methods that are capable of handling occlusions and blur. However, these methods require the images to be geometrically normalized, which is mostly done manually at the moment. In this work, we propose a segmentation and normalization method for ear images that is using cascaded pose regression (CPR). We show that CPR returns accurate rotation and scale estimates, even for full profile images, where the ear has not been segmented yet. We show that the segmentation accuracy of CPR outperforms state of the art detection methods and that CPR improves the recognition rate of an ear recognition system that uses state of the art appearance features.",
"editor": [
{
"familyName": "Bernsmed",
"givenName": "Karin",
"type": "Person"
},
{
"familyName": "Fischer-H\u00fcbner",
"givenName": "Simone",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-319-11599-3_16",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-319-11598-6",
"978-3-319-11599-3"
],
"name": "Secure IT Systems",
"type": "Book"
},
"name": "Segmentation and Normalization of Human Ears Using Cascaded Pose Regression",
"pagination": "261-272",
"productId": [
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-319-11599-3_16"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"324ec50e2c34a21cd1629da1460b38d6e0c2d07df942f727b50b206776050ca1"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1010559835"
]
}
],
"publisher": {
"location": "Cham",
"name": "Springer International Publishing",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-319-11599-3_16",
"https://app.dimensions.ai/details/publication/pub.1010559835"
],
"sdDataset": "chapters",
"sdDatePublished": "2019-04-15T22:53",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8695_00000249.jsonl",
"type": "Chapter",
"url": "http://link.springer.com/10.1007/978-3-319-11599-3_16"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-11599-3_16'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-11599-3_16'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-11599-3_16'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-11599-3_16'
This table displays all metadata directly associated to this object as RDF triples.
122 TRIPLES
23 PREDICATES
41 URIs
20 LITERALS
8 BLANK NODES