2018 |
da Silva, Samira Santos Aggregating Partial Least Squares Models for Open-set Face Identification Masters Thesis Federal University of Minas Gerais, 2018. Resumo | Links | BibTeX | Tags: Face Identification, Open-set Face Recognition, Partial Least Squares @mastersthesis{Silva:2018:MSc, title = {Aggregating Partial Least Squares Models for Open-set Face Identification}, author = {Samira Santos da Silva}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2020/04/dissertation_versaocomfichacatalografica.pdf}, year = {2018}, date = {2018-01-16}, school = {Federal University of Minas Gerais}, abstract = {Face identification is an important task in computer vision and has a myriad of applications, such as in surveillance, forensics and human-computer interaction. In the past few years, several methods have been proposed to solve face identification task in closed-set scenarios, that is, methods that make assumption of all the probe images necessarily matching a gallery individual. However, in real-world applications, one might want to determine the identity of an unknown face in open-set scenarios. In this work, we propose a novel method to perform open-set face identification by aggregating Partial Least Squares models using the one-against-all protocol in a simple but fast way. The model outputs are combined into a response histogram which is balanced if the probe face belongs to a gallery individual or have a highlighted bin, otherwise. Evaluation is performed in four datasets: FRGCv1, FG-NET, Pubfig and Pubfig83. Results show significant improvement when compared to state-of-the art approaches regardless challenges posed by different datasets.}, keywords = {Face Identification, Open-set Face Recognition, Partial Least Squares}, pubstate = {published}, tppubtype = {mastersthesis} } Face identification is an important task in computer vision and has a myriad of applications, such as in surveillance, forensics and human-computer interaction. In the past few years, several methods have been proposed to solve face identification task in closed-set scenarios, that is, methods that make assumption of all the probe images necessarily matching a gallery individual. However, in real-world applications, one might want to determine the identity of an unknown face in open-set scenarios. In this work, we propose a novel method to perform open-set face identification by aggregating Partial Least Squares models using the one-against-all protocol in a simple but fast way. The model outputs are combined into a response histogram which is balanced if the probe face belongs to a gallery individual or have a highlighted bin, otherwise. Evaluation is performed in four datasets: FRGCv1, FG-NET, Pubfig and Pubfig83. Results show significant improvement when compared to state-of-the art approaches regardless challenges posed by different datasets. |
2016 |
Vareto, Rafael Henrique; de Costa, Filipe Oliveira; Schwartz, William Robson Face Identification in Large Galleries Inproceedings Workshop on Face Processing Applications, pp. 1-4, 2016. Links | BibTeX | Tags: DeepEyes, Face Identification, Face Recognition, GigaFrames, VER+ @inproceedings{Vareto:2016:WFPA, title = {Face Identification in Large Galleries}, author = {Rafael Henrique Vareto and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_WFPA.pdf}, year = {2016}, date = {2016-10-02}, booktitle = {Workshop on Face Processing Applications}, pages = {1-4}, keywords = {DeepEyes, Face Identification, Face Recognition, GigaFrames, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
dos Junior, Cassio Elias Santos; Kijak, Ewa; Gravier, Guillaume; Schwartz, William Robson Partial least squares for face hashing Journal Article Neurocomputing, 213 , pp. 34–47, 2016. Links | BibTeX | Tags: Face Identification, Face Recognition, Featured Publication, Partial Least Squares @article{Santos:2016:Neurocomputing, title = {Partial least squares for face hashing}, author = {Cassio Elias Santos dos Junior and Ewa Kijak and Guillaume Gravier and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_Neurocomputing_Santos.pdf}, year = {2016}, date = {2016-02-03}, journal = {Neurocomputing}, volume = {213}, pages = {34--47}, keywords = {Face Identification, Face Recognition, Featured Publication, Partial Least Squares}, pubstate = {published}, tppubtype = {article} } |
2015 |
dos Junior, Cassio Elias Santos Partial Least Squares for Face Hashing Masters Thesis Federal University of Minas Gerais, 2015. Resumo | Links | BibTeX | Tags: DeepEyes, Face Identification, Face Recognition, GigaFrames, Indexing Structure, Local Sensitive Hashing, Partial Least Squares, VER+ @mastersthesis{Santos:2015:MSc, title = {Partial Least Squares for Face Hashing}, author = {Cassio Elias Santos dos Junior}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/dissertation_2015_Cassio.pdf}, year = {2015}, date = {2015-08-24}, school = {Federal University of Minas Gerais}, abstract = {Face identification is an important research topic due to areas such as its application to surveillance, forensics and human-computer interaction. In the past few years, a myriad of methods for face identification has been proposed in the literature, with just a few among them focusing on scalability. In this work, we propose a simple but efficient approach for scalable face identification based on partial least squares (PLS) and random independent hash functions inspired by locality-sensitive hashing (LSH), resulting in the PLS for hashing (PLSH) approach. The original PLSH approach is further extended using feature selection to reduce the computational cost to evaluate the PLS-based hash functions, resulting in the state-of-the-art extended PLSH approach (ePLSH). The proposed approach is evaluated in the dataset FERET and in the dataset FRGCv1. The results show significant reduction in the number of subjects evaluated in the face identification (reduced to 0.3% of the gallery), providing averaged speedups up to 233 times compared to evaluating all subjects in the face gallery and 58 times compared to previous works in the literature.}, keywords = {DeepEyes, Face Identification, Face Recognition, GigaFrames, Indexing Structure, Local Sensitive Hashing, Partial Least Squares, VER+}, pubstate = {published}, tppubtype = {mastersthesis} } Face identification is an important research topic due to areas such as its application to surveillance, forensics and human-computer interaction. In the past few years, a myriad of methods for face identification has been proposed in the literature, with just a few among them focusing on scalability. In this work, we propose a simple but efficient approach for scalable face identification based on partial least squares (PLS) and random independent hash functions inspired by locality-sensitive hashing (LSH), resulting in the PLS for hashing (PLSH) approach. The original PLSH approach is further extended using feature selection to reduce the computational cost to evaluate the PLS-based hash functions, resulting in the state-of-the-art extended PLSH approach (ePLSH). The proposed approach is evaluated in the dataset FERET and in the dataset FRGCv1. The results show significant reduction in the number of subjects evaluated in the face identification (reduced to 0.3% of the gallery), providing averaged speedups up to 233 times compared to evaluating all subjects in the face gallery and 58 times compared to previous works in the literature. |
dos Junior, Cassio Elias Santos; Kijak, E; Gravier, G; Schwartz, William Robson Learning to Hash Faces Using Large Feature Vectors Inproceedings Content-Based Multimedia Indexing (CBMI), 13th International Workshop on, pp. 1–6, IEEE, 2015. Links | BibTeX | Tags: Face Identification, Face Recognition, GigaFrames, Indexing Structure, Locality Sensitive Hashing, Partial Least Squares, SmartView, VER+ @inproceedings{santos2015learning, title = {Learning to Hash Faces Using Large Feature Vectors}, author = {Cassio Elias Santos dos Junior and E Kijak and G Gravier and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2015-Learning_to_Hash_Faces_Using_Large_Feature_Vectors.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Content-Based Multimedia Indexing (CBMI), 13th International Workshop on}, pages = {1--6}, publisher = {IEEE}, keywords = {Face Identification, Face Recognition, GigaFrames, Indexing Structure, Locality Sensitive Hashing, Partial Least Squares, SmartView, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
de Carlos, Gerson Paulo; Pedrini, Helio; Schwartz, William Robson Classification schemes based on Partial Least Squares for face identification Journal Article Journal of Visual Communication and Image Representation, 32 , pp. 170 - 179, 2015, ISSN: 1047-3203. Links | BibTeX | Tags: Face Identification, Face Recognition, One-Against-All Classification Scheme, Partial Least Squares, VER+ @article{2015:JVCI:Carlos, title = {Classification schemes based on Partial Least Squares for face identification}, author = {Gerson Paulo de Carlos and Helio Pedrini and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_JVCI.pdf}, issn = {1047-3203}, year = {2015}, date = {2015-01-01}, journal = {Journal of Visual Communication and Image Representation}, volume = {32}, pages = {170 - 179}, keywords = {Face Identification, Face Recognition, One-Against-All Classification Scheme, Partial Least Squares, VER+}, pubstate = {published}, tppubtype = {article} } |
2014 |
dos Junior, Cassio Elias Santos; Schwartz, William Robson Extending Face Identification to Open-Set Face Recognition Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2014. Links | BibTeX | Tags: ARDOP, Face Identification, Face Recognition, Open-Set Classification, SmartView @inproceedings{sibgrapi2014extending, title = {Extending Face Identification to Open-Set Face Recognition}, author = {Cassio Elias Santos dos Junior and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2014-Extending-Face-Identification-to-Open-Set-Face-Recognition.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {ARDOP, Face Identification, Face Recognition, Open-Set Classification, SmartView}, pubstate = {published}, tppubtype = {inproceedings} } |
2013 |
Carlos, G P; Pedrini, H; Schwartz, William Robson Fast and Scalable Enrollment for Face Identification based on Partial Least Squares Inproceedings IEEE International Conference on Automatic Face and Gesture Recognition, 2013. Links | BibTeX | Tags: ARDOP, Face Identification, Face Recognition, One-Against-Some Classification Scheme @inproceedings{Carlos:2013:FG, title = {Fast and Scalable Enrollment for Face Identification based on Partial Least Squares}, author = {G P Carlos and H Pedrini and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2013_FG.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {IEEE International Conference on Automatic Face and Gesture Recognition}, keywords = {ARDOP, Face Identification, Face Recognition, One-Against-Some Classification Scheme}, pubstate = {published}, tppubtype = {inproceedings} } |
2012 |
Schwartz, William Robson; Guo, Huimin; Choi, Jonghyun; Davis, Larry S Face identification using large feature sets Journal Article Image Processing, IEEE Transactions on, 21 (4), pp. 2245–2255, 2012. Links | BibTeX | Tags: Face Identification, Face Recognition, Indexing Structure, Partial Least Squares, PFI @article{schwartz2012face, title = {Face identification using large feature sets}, author = {William Robson Schwartz and Huimin Guo and Jonghyun Choi and Larry S Davis}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2012-Face-identification-using-large-feature-sets.pdf}, year = {2012}, date = {2012-01-01}, journal = {Image Processing, IEEE Transactions on}, volume = {21}, number = {4}, pages = {2245--2255}, publisher = {IEEE url = https://googledrive.com/host/0B53xd8WZN11YeTRvemlqYkJNd0E/paper_2011_TIP.pdf}, keywords = {Face Identification, Face Recognition, Indexing Structure, Partial Least Squares, PFI}, pubstate = {published}, tppubtype = {article} } |
Choi, J; Guo, H; Schwartz, William Robson; Davis, L S A Complementary Local Feature Descriptor for Face Identification Inproceedings IEEE Workshop on Applications of Computer Vision, pp. 121-128, 2012. Links | BibTeX | Tags: CCS-POP, Face Identification, Face Recognition, Feature Extraction @inproceedings{Choi:2012:WACV, title = {A Complementary Local Feature Descriptor for Face Identification}, author = {J Choi and H Guo and William Robson Schwartz and L S Davis}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2012-A-Complementary-Local-Feature-Descriptor-for-Face-Identification.pdf}, year = {2012}, date = {2012-01-01}, booktitle = {IEEE Workshop on Applications of Computer Vision}, pages = {121-128}, keywords = {CCS-POP, Face Identification, Face Recognition, Feature Extraction}, pubstate = {published}, tppubtype = {inproceedings} } |
2010 |
Schwartz, William Robson; Guo, H; Davis, L S A Robust and Scalable Approach to Face Identification Inproceedings European Conference on Computer Vision, pp. 476-489, 2010. Links | BibTeX | Tags: Face Identification, Face Recognition, One-Against-All Classification Scheme, Partial Least Squares, PFI @inproceedings{Schwartz:2010:ECCV, title = {A Robust and Scalable Approach to Face Identification}, author = {William Robson Schwartz and H Guo and L S Davis}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2010-A-Robust-and-Scalable-Approach-to-Face-Identification.pdf}, year = {2010}, date = {2010-01-01}, booktitle = {European Conference on Computer Vision}, volume = {6316}, pages = {476-489}, series = {Lecture Notes in Computer Science}, keywords = {Face Identification, Face Recognition, One-Against-All Classification Scheme, Partial Least Squares, PFI}, pubstate = {published}, tppubtype = {inproceedings} } |
2018 |
Samira Santos da Silva Aggregating Partial Least Squares Models for Open-set Face Identification Masters Thesis Federal University of Minas Gerais, 2018. Resumo | Links | BibTeX | Tags: Face Identification, Open-set Face Recognition, Partial Least Squares @mastersthesis{Silva:2018:MSc, title = {Aggregating Partial Least Squares Models for Open-set Face Identification}, author = {Samira Santos da Silva}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2020/04/dissertation_versaocomfichacatalografica.pdf}, year = {2018}, date = {2018-01-16}, school = {Federal University of Minas Gerais}, abstract = {Face identification is an important task in computer vision and has a myriad of applications, such as in surveillance, forensics and human-computer interaction. In the past few years, several methods have been proposed to solve face identification task in closed-set scenarios, that is, methods that make assumption of all the probe images necessarily matching a gallery individual. However, in real-world applications, one might want to determine the identity of an unknown face in open-set scenarios. In this work, we propose a novel method to perform open-set face identification by aggregating Partial Least Squares models using the one-against-all protocol in a simple but fast way. The model outputs are combined into a response histogram which is balanced if the probe face belongs to a gallery individual or have a highlighted bin, otherwise. Evaluation is performed in four datasets: FRGCv1, FG-NET, Pubfig and Pubfig83. Results show significant improvement when compared to state-of-the art approaches regardless challenges posed by different datasets.}, keywords = {Face Identification, Open-set Face Recognition, Partial Least Squares}, pubstate = {published}, tppubtype = {mastersthesis} } Face identification is an important task in computer vision and has a myriad of applications, such as in surveillance, forensics and human-computer interaction. In the past few years, several methods have been proposed to solve face identification task in closed-set scenarios, that is, methods that make assumption of all the probe images necessarily matching a gallery individual. However, in real-world applications, one might want to determine the identity of an unknown face in open-set scenarios. In this work, we propose a novel method to perform open-set face identification by aggregating Partial Least Squares models using the one-against-all protocol in a simple but fast way. The model outputs are combined into a response histogram which is balanced if the probe face belongs to a gallery individual or have a highlighted bin, otherwise. Evaluation is performed in four datasets: FRGCv1, FG-NET, Pubfig and Pubfig83. Results show significant improvement when compared to state-of-the art approaches regardless challenges posed by different datasets. |
2016 |
Rafael Henrique Vareto; Filipe Oliveira de Costa; William Robson Schwartz Face Identification in Large Galleries Inproceedings Workshop on Face Processing Applications, pp. 1-4, 2016. Links | BibTeX | Tags: DeepEyes, Face Identification, Face Recognition, GigaFrames, VER+ @inproceedings{Vareto:2016:WFPA, title = {Face Identification in Large Galleries}, author = {Rafael Henrique Vareto and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_WFPA.pdf}, year = {2016}, date = {2016-10-02}, booktitle = {Workshop on Face Processing Applications}, pages = {1-4}, keywords = {DeepEyes, Face Identification, Face Recognition, GigaFrames, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
Cassio Elias Santos dos Junior; Ewa Kijak; Guillaume Gravier; William Robson Schwartz Partial least squares for face hashing Journal Article Neurocomputing, 213 , pp. 34–47, 2016. Links | BibTeX | Tags: Face Identification, Face Recognition, Featured Publication, Partial Least Squares @article{Santos:2016:Neurocomputing, title = {Partial least squares for face hashing}, author = {Cassio Elias Santos dos Junior and Ewa Kijak and Guillaume Gravier and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_Neurocomputing_Santos.pdf}, year = {2016}, date = {2016-02-03}, journal = {Neurocomputing}, volume = {213}, pages = {34--47}, keywords = {Face Identification, Face Recognition, Featured Publication, Partial Least Squares}, pubstate = {published}, tppubtype = {article} } |
2015 |
Cassio Elias Santos dos Junior Partial Least Squares for Face Hashing Masters Thesis Federal University of Minas Gerais, 2015. Resumo | Links | BibTeX | Tags: DeepEyes, Face Identification, Face Recognition, GigaFrames, Indexing Structure, Local Sensitive Hashing, Partial Least Squares, VER+ @mastersthesis{Santos:2015:MSc, title = {Partial Least Squares for Face Hashing}, author = {Cassio Elias Santos dos Junior}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/dissertation_2015_Cassio.pdf}, year = {2015}, date = {2015-08-24}, school = {Federal University of Minas Gerais}, abstract = {Face identification is an important research topic due to areas such as its application to surveillance, forensics and human-computer interaction. In the past few years, a myriad of methods for face identification has been proposed in the literature, with just a few among them focusing on scalability. In this work, we propose a simple but efficient approach for scalable face identification based on partial least squares (PLS) and random independent hash functions inspired by locality-sensitive hashing (LSH), resulting in the PLS for hashing (PLSH) approach. The original PLSH approach is further extended using feature selection to reduce the computational cost to evaluate the PLS-based hash functions, resulting in the state-of-the-art extended PLSH approach (ePLSH). The proposed approach is evaluated in the dataset FERET and in the dataset FRGCv1. The results show significant reduction in the number of subjects evaluated in the face identification (reduced to 0.3% of the gallery), providing averaged speedups up to 233 times compared to evaluating all subjects in the face gallery and 58 times compared to previous works in the literature.}, keywords = {DeepEyes, Face Identification, Face Recognition, GigaFrames, Indexing Structure, Local Sensitive Hashing, Partial Least Squares, VER+}, pubstate = {published}, tppubtype = {mastersthesis} } Face identification is an important research topic due to areas such as its application to surveillance, forensics and human-computer interaction. In the past few years, a myriad of methods for face identification has been proposed in the literature, with just a few among them focusing on scalability. In this work, we propose a simple but efficient approach for scalable face identification based on partial least squares (PLS) and random independent hash functions inspired by locality-sensitive hashing (LSH), resulting in the PLS for hashing (PLSH) approach. The original PLSH approach is further extended using feature selection to reduce the computational cost to evaluate the PLS-based hash functions, resulting in the state-of-the-art extended PLSH approach (ePLSH). The proposed approach is evaluated in the dataset FERET and in the dataset FRGCv1. The results show significant reduction in the number of subjects evaluated in the face identification (reduced to 0.3% of the gallery), providing averaged speedups up to 233 times compared to evaluating all subjects in the face gallery and 58 times compared to previous works in the literature. |
Cassio Elias Santos dos Junior; E Kijak; G Gravier; William Robson Schwartz Learning to Hash Faces Using Large Feature Vectors Inproceedings Content-Based Multimedia Indexing (CBMI), 13th International Workshop on, pp. 1–6, IEEE, 2015. Links | BibTeX | Tags: Face Identification, Face Recognition, GigaFrames, Indexing Structure, Locality Sensitive Hashing, Partial Least Squares, SmartView, VER+ @inproceedings{santos2015learning, title = {Learning to Hash Faces Using Large Feature Vectors}, author = {Cassio Elias Santos dos Junior and E Kijak and G Gravier and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2015-Learning_to_Hash_Faces_Using_Large_Feature_Vectors.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Content-Based Multimedia Indexing (CBMI), 13th International Workshop on}, pages = {1--6}, publisher = {IEEE}, keywords = {Face Identification, Face Recognition, GigaFrames, Indexing Structure, Locality Sensitive Hashing, Partial Least Squares, SmartView, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
Gerson Paulo de Carlos; Helio Pedrini; William Robson Schwartz Classification schemes based on Partial Least Squares for face identification Journal Article Journal of Visual Communication and Image Representation, 32 , pp. 170 - 179, 2015, ISSN: 1047-3203. Links | BibTeX | Tags: Face Identification, Face Recognition, One-Against-All Classification Scheme, Partial Least Squares, VER+ @article{2015:JVCI:Carlos, title = {Classification schemes based on Partial Least Squares for face identification}, author = {Gerson Paulo de Carlos and Helio Pedrini and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_JVCI.pdf}, issn = {1047-3203}, year = {2015}, date = {2015-01-01}, journal = {Journal of Visual Communication and Image Representation}, volume = {32}, pages = {170 - 179}, keywords = {Face Identification, Face Recognition, One-Against-All Classification Scheme, Partial Least Squares, VER+}, pubstate = {published}, tppubtype = {article} } |
2014 |
Cassio Elias Santos dos Junior; William Robson Schwartz Extending Face Identification to Open-Set Face Recognition Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2014. Links | BibTeX | Tags: ARDOP, Face Identification, Face Recognition, Open-Set Classification, SmartView @inproceedings{sibgrapi2014extending, title = {Extending Face Identification to Open-Set Face Recognition}, author = {Cassio Elias Santos dos Junior and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2014-Extending-Face-Identification-to-Open-Set-Face-Recognition.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {ARDOP, Face Identification, Face Recognition, Open-Set Classification, SmartView}, pubstate = {published}, tppubtype = {inproceedings} } |
2013 |
G P Carlos; H Pedrini; William Robson Schwartz Fast and Scalable Enrollment for Face Identification based on Partial Least Squares Inproceedings IEEE International Conference on Automatic Face and Gesture Recognition, 2013. Links | BibTeX | Tags: ARDOP, Face Identification, Face Recognition, One-Against-Some Classification Scheme @inproceedings{Carlos:2013:FG, title = {Fast and Scalable Enrollment for Face Identification based on Partial Least Squares}, author = {G P Carlos and H Pedrini and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2013_FG.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {IEEE International Conference on Automatic Face and Gesture Recognition}, keywords = {ARDOP, Face Identification, Face Recognition, One-Against-Some Classification Scheme}, pubstate = {published}, tppubtype = {inproceedings} } |
2012 |
William Robson Schwartz; Huimin Guo; Jonghyun Choi; Larry S Davis Face identification using large feature sets Journal Article Image Processing, IEEE Transactions on, 21 (4), pp. 2245–2255, 2012. Links | BibTeX | Tags: Face Identification, Face Recognition, Indexing Structure, Partial Least Squares, PFI @article{schwartz2012face, title = {Face identification using large feature sets}, author = {William Robson Schwartz and Huimin Guo and Jonghyun Choi and Larry S Davis}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2012-Face-identification-using-large-feature-sets.pdf}, year = {2012}, date = {2012-01-01}, journal = {Image Processing, IEEE Transactions on}, volume = {21}, number = {4}, pages = {2245--2255}, publisher = {IEEE url = https://googledrive.com/host/0B53xd8WZN11YeTRvemlqYkJNd0E/paper_2011_TIP.pdf}, keywords = {Face Identification, Face Recognition, Indexing Structure, Partial Least Squares, PFI}, pubstate = {published}, tppubtype = {article} } |
J Choi; H Guo; William Robson Schwartz; L S Davis A Complementary Local Feature Descriptor for Face Identification Inproceedings IEEE Workshop on Applications of Computer Vision, pp. 121-128, 2012. Links | BibTeX | Tags: CCS-POP, Face Identification, Face Recognition, Feature Extraction @inproceedings{Choi:2012:WACV, title = {A Complementary Local Feature Descriptor for Face Identification}, author = {J Choi and H Guo and William Robson Schwartz and L S Davis}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2012-A-Complementary-Local-Feature-Descriptor-for-Face-Identification.pdf}, year = {2012}, date = {2012-01-01}, booktitle = {IEEE Workshop on Applications of Computer Vision}, pages = {121-128}, keywords = {CCS-POP, Face Identification, Face Recognition, Feature Extraction}, pubstate = {published}, tppubtype = {inproceedings} } |
2010 |
William Robson Schwartz; H Guo; L S Davis A Robust and Scalable Approach to Face Identification Inproceedings European Conference on Computer Vision, pp. 476-489, 2010. Links | BibTeX | Tags: Face Identification, Face Recognition, One-Against-All Classification Scheme, Partial Least Squares, PFI @inproceedings{Schwartz:2010:ECCV, title = {A Robust and Scalable Approach to Face Identification}, author = {William Robson Schwartz and H Guo and L S Davis}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2010-A-Robust-and-Scalable-Approach-to-Face-Identification.pdf}, year = {2010}, date = {2010-01-01}, booktitle = {European Conference on Computer Vision}, volume = {6316}, pages = {476-489}, series = {Lecture Notes in Computer Science}, keywords = {Face Identification, Face Recognition, One-Against-All Classification Scheme, Partial Least Squares, PFI}, pubstate = {published}, tppubtype = {inproceedings} } |