George Azzopardi; Antonio Greco; Mario Vento Gender recognition from face images with trainable COSFIRE filters Inproceedings 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 235-241, 2016. Abstract | BibTeX | Tag: | Links: @inproceedings{7738068,
title = {Gender recognition from face images with trainable COSFIRE filters},
author = {George Azzopardi and Antonio Greco and Mario Vento},
doi = {10.1109/AVSS.2016.7738068},
year = {2016},
date = {2016-08-01},
booktitle = {2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
pages = {235-241},
abstract = {Gender recognition from face images is an important application in the fields of security, retail advertising and marketing. We propose a novel descriptor based on COSFIRE filters for gender recognition. A COSFIRE filter is trainable, in that its selectivity is determined in an automatic configuration process that analyses a given prototype pattern of interest. We demonstrate the effectiveness of the proposed approach on a new dataset called GENDER-FERET with 474 training and 472 test samples and achieve an accuracy rate of 93.7%. It also outperforms an approach that relies on handcrafted features and an ensemble of classifiers. Furthermore, we perform another experiment by using the images of the Labeled Faces in the Wild (LFW) dataset to train our classifier and the test images of the GENDER-FERET dataset for evaluation. This experiment demonstrates the generalization ability of the proposed approach and it also outperforms two commercial libraries, namely Face++ and Luxand.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Gender recognition from face images is an important application in the fields of security, retail advertising and marketing. We propose a novel descriptor based on COSFIRE filters for gender recognition. A COSFIRE filter is trainable, in that its selectivity is determined in an automatic configuration process that analyses a given prototype pattern of interest. We demonstrate the effectiveness of the proposed approach on a new dataset called GENDER-FERET with 474 training and 472 test samples and achieve an accuracy rate of 93.7%. It also outperforms an approach that relies on handcrafted features and an ensemble of classifiers. Furthermore, we perform another experiment by using the images of the Labeled Faces in the Wild (LFW) dataset to train our classifier and the test images of the GENDER-FERET dataset for evaluation. This experiment demonstrates the generalization ability of the proposed approach and it also outperforms two commercial libraries, namely Face++ and Luxand. |
George Azzopardi; Antonio Greco; Mario Vento Gender Recognition from Face Images Using a Fusion of SVM Classifiers Book Chapter Campilho, Aurélio; Karray, Fakhri (Ed.): Image Analysis and Recognition: 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, P'ovoa de Varzim, Portugal, July 13-15, 2016, Proceedings, pp. 533–538, Springer International Publishing, Cham, 2016, ISBN: 978-3-319-41501-7. Abstract | BibTeX | Tag: | Links: @inbook{Azzopardi2016,
title = {Gender Recognition from Face Images Using a Fusion of SVM Classifiers},
author = {George Azzopardi and Antonio Greco and Mario Vento},
editor = {Aurélio Campilho and Fakhri Karray},
url = {http://dx.doi.org/10.1007/978-3-319-41501-7_59},
doi = {10.1007/978-3-319-41501-7_59},
isbn = {978-3-319-41501-7},
year = {2016},
date = {2016-01-01},
booktitle = {Image Analysis and Recognition: 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, P'ovoa de Varzim, Portugal, July 13-15, 2016, Proceedings},
pages = {533--538},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The recognition of gender from face images is an important application, especially in the fields of security, marketing and intelligent user interfaces. We propose an approach to gender recognition from faces by fusing the decisions of SVM classifiers. Each classifier is trained with different types of features, namely HOG (shape), LBP (texture) and raw pixel values. For the latter features we use an SVM with a linear kernel and for the two former ones we use SVMs with histogram intersection kernels. We come to a decision by fusing the three classifiers with a majority vote. We demonstrate the effectiveness of our approach on a new dataset that we extract from FERET. We achieve an accuracy of 92.6 %, which outperforms the commercial products Face++ and Luxand.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
The recognition of gender from face images is an important application, especially in the fields of security, marketing and intelligent user interfaces. We propose an approach to gender recognition from faces by fusing the decisions of SVM classifiers. Each classifier is trained with different types of features, namely HOG (shape), LBP (texture) and raw pixel values. For the latter features we use an SVM with a linear kernel and for the two former ones we use SVMs with histogram intersection kernels. We come to a decision by fusing the three classifiers with a majority vote. We demonstrate the effectiveness of our approach on a new dataset that we extract from FERET. We achieve an accuracy of 92.6 %, which outperforms the commercial products Face++ and Luxand. |
Vincenzo Carletti; Pasquale Foggia; Antonio Greco; Alessia Saggese; Mario Vento Automatic detection of long-term parked cars Conference 12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015, 2015. BibTeX | Tag: Video analysis and interpretation @conference{CarlettiPark15,
title = {Automatic detection of long-term parked cars},
author = {Vincenzo Carletti and Pasquale Foggia and Antonio Greco and Alessia Saggese and Mario Vento},
year = {2015},
date = {2015-08-25},
booktitle = {12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {conference}
}
|
Pasquale Foggia; Antonio Greco; Alessia Saggese; Mario Vento A method for detecting long term left baggage based on heat map Inproceedings VISAPP 2015, 2015. BibTeX | Tag: Video analysis and interpretation @inproceedings{visapp15_lb,
title = {A method for detecting long term left baggage based on heat map},
author = {Pasquale Foggia and Antonio Greco and Alessia Saggese and Mario Vento},
year = {2015},
date = {2015-03-12},
booktitle = {VISAPP 2015},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Rosario Di Lascio; Antonio Greco; Alessia Saggese; Mario Vento Improving fire detection reliability by a combination of videoanalytics Incollection Publishing, Springer International (Ed.): Image Analysis and Recognition, pp. 477-484, 2014, ISBN: 978-3-319-11757-7. BibTeX | Tag: Video analysis and interpretation | Links: @incollection{iciar2014,
title = {Improving fire detection reliability by a combination of videoanalytics},
author = {Rosario Di Lascio and Antonio Greco and Alessia Saggese and Mario Vento},
editor = {Springer International Publishing},
url = {http://dx.doi.org/10.1007/978-3-319-11758-4_52},
isbn = {978-3-319-11757-7},
year = {2014},
date = {2014-10-15},
booktitle = {Image Analysis and Recognition},
pages = {477-484},
keywords = {Video analysis and interpretation},
pubstate = {published},
tppubtype = {incollection}
}
|
