Recognizing Handshapes using Small Datasets
- Autores
- Cornejo Fandos, Ulises Jeremias; Ríos, Gastón Gustavo; Ronchetti, Franco; Quiroga, Facundo; Hasperué, Waldo; Lanzarini, Laura Cristina
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Advances in convolutional neural networks have made possible significant improvements in the state-of-the-art in image classification. However, their success on a particular field rests on the possibility of obtaining labeled data to train networks. Handshape recognition from images, an important subtask of both gesture and sign language recognition, suffers from such a lack of data. Furthermore, hands are highly deformable objects and therefore handshape classification models require larger datasets. We analyze both state of the art models for image classification, as well as data augmentation schemes and specific models to tackle problems with small datasets. In particular, we perform experiments with Wide- DenseNet, a state of the art convolutional architecture and Prototypical Networks, a state of the art few-shot learning meta model. In both cases, we also quantify the impact of data augmentation on accuracy. Our results show that on small and simple data sets such as CIARP, all models and variations of achieve perfect accuracy, and therefore the utility of the data is highly doubtful, despite its having 6000 samples. On the other hand, in small but complex datasets such as LSA16 (800 samples), specialized methods such as Prototypical Networks do have an advantage over other methods. On RWTH, another complex and small dataset with close to 4000 samples, a traditional and state-of-the-art method such as Wide-DenseNet surpasses all other models. Also, data augmentation consistently increases accuracy for Wide-DenseNet, but not fo Prototypical Networks.
XX Workshop de Agentes y Sistemas inteligentes.
Red de Universidades con Carreras en Informática - Materia
-
Ciencias Informáticas
Sign Language
Hand Shape Recognition
Convolutional Neural Networks
Densenet
Prototypical Networks
Small Datasets - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/90457
Ver los metadatos del registro completo
id |
SEDICI_0c9515bea8236a04e3e8225d0da00ca9 |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/90457 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
spelling |
Recognizing Handshapes using Small DatasetsCornejo Fandos, Ulises JeremiasRíos, Gastón GustavoRonchetti, FrancoQuiroga, FacundoHasperué, WaldoLanzarini, Laura CristinaCiencias InformáticasSign LanguageHand Shape RecognitionConvolutional Neural NetworksDensenetPrototypical NetworksSmall DatasetsAdvances in convolutional neural networks have made possible significant improvements in the state-of-the-art in image classification. However, their success on a particular field rests on the possibility of obtaining labeled data to train networks. Handshape recognition from images, an important subtask of both gesture and sign language recognition, suffers from such a lack of data. Furthermore, hands are highly deformable objects and therefore handshape classification models require larger datasets. We analyze both state of the art models for image classification, as well as data augmentation schemes and specific models to tackle problems with small datasets. In particular, we perform experiments with Wide- DenseNet, a state of the art convolutional architecture and Prototypical Networks, a state of the art few-shot learning meta model. In both cases, we also quantify the impact of data augmentation on accuracy. Our results show that on small and simple data sets such as CIARP, all models and variations of achieve perfect accuracy, and therefore the utility of the data is highly doubtful, despite its having 6000 samples. On the other hand, in small but complex datasets such as LSA16 (800 samples), specialized methods such as Prototypical Networks do have an advantage over other methods. On RWTH, another complex and small dataset with close to 4000 samples, a traditional and state-of-the-art method such as Wide-DenseNet surpasses all other models. Also, data augmentation consistently increases accuracy for Wide-DenseNet, but not fo Prototypical Networks.XX Workshop de Agentes y Sistemas inteligentes.Red de Universidades con Carreras en Informática2019-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf105-114http://sedici.unlp.edu.ar/handle/10915/90457enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1info:eu-repo/semantics/reference/hdl/10915/90359info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:18:37Zoai:sedici.unlp.edu.ar:10915/90457Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:18:37.81SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Recognizing Handshapes using Small Datasets |
title |
Recognizing Handshapes using Small Datasets |
spellingShingle |
Recognizing Handshapes using Small Datasets Cornejo Fandos, Ulises Jeremias Ciencias Informáticas Sign Language Hand Shape Recognition Convolutional Neural Networks Densenet Prototypical Networks Small Datasets |
title_short |
Recognizing Handshapes using Small Datasets |
title_full |
Recognizing Handshapes using Small Datasets |
title_fullStr |
Recognizing Handshapes using Small Datasets |
title_full_unstemmed |
Recognizing Handshapes using Small Datasets |
title_sort |
Recognizing Handshapes using Small Datasets |
dc.creator.none.fl_str_mv |
Cornejo Fandos, Ulises Jeremias Ríos, Gastón Gustavo Ronchetti, Franco Quiroga, Facundo Hasperué, Waldo Lanzarini, Laura Cristina |
author |
Cornejo Fandos, Ulises Jeremias |
author_facet |
Cornejo Fandos, Ulises Jeremias Ríos, Gastón Gustavo Ronchetti, Franco Quiroga, Facundo Hasperué, Waldo Lanzarini, Laura Cristina |
author_role |
author |
author2 |
Ríos, Gastón Gustavo Ronchetti, Franco Quiroga, Facundo Hasperué, Waldo Lanzarini, Laura Cristina |
author2_role |
author author author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Sign Language Hand Shape Recognition Convolutional Neural Networks Densenet Prototypical Networks Small Datasets |
topic |
Ciencias Informáticas Sign Language Hand Shape Recognition Convolutional Neural Networks Densenet Prototypical Networks Small Datasets |
dc.description.none.fl_txt_mv |
Advances in convolutional neural networks have made possible significant improvements in the state-of-the-art in image classification. However, their success on a particular field rests on the possibility of obtaining labeled data to train networks. Handshape recognition from images, an important subtask of both gesture and sign language recognition, suffers from such a lack of data. Furthermore, hands are highly deformable objects and therefore handshape classification models require larger datasets. We analyze both state of the art models for image classification, as well as data augmentation schemes and specific models to tackle problems with small datasets. In particular, we perform experiments with Wide- DenseNet, a state of the art convolutional architecture and Prototypical Networks, a state of the art few-shot learning meta model. In both cases, we also quantify the impact of data augmentation on accuracy. Our results show that on small and simple data sets such as CIARP, all models and variations of achieve perfect accuracy, and therefore the utility of the data is highly doubtful, despite its having 6000 samples. On the other hand, in small but complex datasets such as LSA16 (800 samples), specialized methods such as Prototypical Networks do have an advantage over other methods. On RWTH, another complex and small dataset with close to 4000 samples, a traditional and state-of-the-art method such as Wide-DenseNet surpasses all other models. Also, data augmentation consistently increases accuracy for Wide-DenseNet, but not fo Prototypical Networks. XX Workshop de Agentes y Sistemas inteligentes. Red de Universidades con Carreras en Informática |
description |
Advances in convolutional neural networks have made possible significant improvements in the state-of-the-art in image classification. However, their success on a particular field rests on the possibility of obtaining labeled data to train networks. Handshape recognition from images, an important subtask of both gesture and sign language recognition, suffers from such a lack of data. Furthermore, hands are highly deformable objects and therefore handshape classification models require larger datasets. We analyze both state of the art models for image classification, as well as data augmentation schemes and specific models to tackle problems with small datasets. In particular, we perform experiments with Wide- DenseNet, a state of the art convolutional architecture and Prototypical Networks, a state of the art few-shot learning meta model. In both cases, we also quantify the impact of data augmentation on accuracy. Our results show that on small and simple data sets such as CIARP, all models and variations of achieve perfect accuracy, and therefore the utility of the data is highly doubtful, despite its having 6000 samples. On the other hand, in small but complex datasets such as LSA16 (800 samples), specialized methods such as Prototypical Networks do have an advantage over other methods. On RWTH, another complex and small dataset with close to 4000 samples, a traditional and state-of-the-art method such as Wide-DenseNet surpasses all other models. Also, data augmentation consistently increases accuracy for Wide-DenseNet, but not fo Prototypical Networks. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/90457 |
url |
http://sedici.unlp.edu.ar/handle/10915/90457 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1 info:eu-repo/semantics/reference/hdl/10915/90359 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf 105-114 |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1844616059763556352 |
score |
13.070432 |