2021
1.
Velasco-Mata, J.; Chaves, D.; de Mata, V.; Al-Nabki, M. W.; Fidalgo, Eduardo; Alegre, Enrique; Azzopardi, George
Development of a Hardware Benchmark for Forensic Face Detection Applications Inproceedings
Abstract | Links | BibTeX | Tags: face analysis, forensic image analysis
@inproceedings{velasco2021development,
title = {Development of a Hardware Benchmark for Forensic Face Detection Applications},
author = {J. Velasco-Mata and D. Chaves and V. de Mata and M. W. Al-Nabki and Eduardo Fidalgo and Enrique Alegre and George Azzopardi
},
url = {https://ruidera.uclm.es/xmlui/handle/10578/28635},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Cybersecurity Research National Conferences - INCIBE, Leon, Spain},
organization = {JNIC},
abstract = {Face detection techniques are valuable in forensic investigation since they help criminal investigators to identify victims/offenders in child sexual exploitation material. Deep learning approaches proved successful in these tasks, but their high computational requirements make them unsuitable if there are time constraints. To cope with this problem, we use a resizing strategy over three face detection techniques \textemdashMTCNN, PyramidBox and DSFD\textemdash to improve their speed over samples selected from the WIDER Face and UFDD datasets across several CPUs and GPUs. The best speed-detection trade-off was achieved by reducing the images to 50% of their original size and then applying DSFD. The fastest hardware for this purpose was a Nvidia GPU based on the Turing architecture.},
keywords = {face analysis, forensic image analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Face detection techniques are valuable in forensic investigation since they help criminal investigators to identify victims/offenders in child sexual exploitation material. Deep learning approaches proved successful in these tasks, but their high computational requirements make them unsuitable if there are time constraints. To cope with this problem, we use a resizing strategy over three face detection techniques —MTCNN, PyramidBox and DSFD— to improve their speed over samples selected from the WIDER Face and UFDD datasets across several CPUs and GPUs. The best speed-detection trade-off was achieved by reducing the images to 50% of their original size and then applying DSFD. The fastest hardware for this purpose was a Nvidia GPU based on the Turing architecture.
2.
Biswas, R.; Chaves, D.; Jáñez-Martino, F.; Blanco-Medina, P.; Fidalgo, E.; García-Olalla, C.; Azzopardi, G.
Reinforcement of age estimation in forensic tools to detect Child Sexual Exploitation Material Inproceedings
Abstract | Links | BibTeX | Tags: face analysis, forensic image analysis
@inproceedings{biswas2021reinforcement,
title = {Reinforcement of age estimation in forensic tools to detect Child Sexual Exploitation Material},
author = {R. Biswas and D. Chaves and F. J\'{a}\~{n}ez-Martino and P. Blanco-Medina and E. Fidalgo and C. Garc\'{i}a-Olalla and G. Azzopardi},
url = {https://ruidera.uclm.es/xmlui/handle/10578/28631},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
organization = {Cybersecurity Research National Conferences},
abstract = {Several image-based approaches for estimating the age of a person are available in computer vision literature. However, most of them perform poorly on minors and young adults, especially when the eyes are occluded. This type of occlusion is common in Child Sexual Exploitation Materials (CSEM), in order to hide the identity of victims. We introduce an approach that builds Soft Stagewise Regression Network (SSR-Net) models with natural and eye-occluded facial images, to estimate the age of minors and young adults. Our proposal reduces the Mean Absolute Error from 7.26 to 6.5, and 6.81 to 4.07 for SSR-Net pre-trained models on the IMDB and MORPH datasets, respectively.},
keywords = {face analysis, forensic image analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Several image-based approaches for estimating the age of a person are available in computer vision literature. However, most of them perform poorly on minors and young adults, especially when the eyes are occluded. This type of occlusion is common in Child Sexual Exploitation Materials (CSEM), in order to hide the identity of victims. We introduce an approach that builds Soft Stagewise Regression Network (SSR-Net) models with natural and eye-occluded facial images, to estimate the age of minors and young adults. Our proposal reduces the Mean Absolute Error from 7.26 to 6.5, and 6.81 to 4.07 for SSR-Net pre-trained models on the IMDB and MORPH datasets, respectively.
2019
3.
Demajo, Lara Marie; Guillaumier, Kristian; Azzopardi, George
Age group recognition from face images using a fusion of CNN-and COSFIRE-based features Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, convnets, deep learning, face analysis, trainable filters
@inproceedings{demajo2019age,
title = {Age group recognition from face images using a fusion of CNN-and COSFIRE-based features},
author = {Lara Marie Demajo and Kristian Guillaumier and George Azzopardi},
doi = {https://doi.org/10.1145/3309772.3309784},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {Proceedings of the 2nd International Conference on Applications of Intelligent Systems},
pages = {1--6},
abstract = {Automatic age group classification is the ability of an algorithm to classify face images into predetermined age groups. It is an important task due to its numerous applications such as monitoring, biometrics and commercial profiling. In this work we propose a fusion technique that combines CNN- and COSFIRE-based features for the recognition of age groups from face images. Both CNN and COSFIRE are trainable approaches that have been demonstrated to be effective in various computer vision applications. As to CNN, we use the pre-trained VGG-Face architecture and for COSFIRE we configure new COSFIRE filters from training data. Since recent literature suggests that CNNs deliver the highest accuracy rates within such problems, the hypothesis which we want to investigate in this work is whether combining CNN and COSFIRE approaches together will improve results. The proposed fusion technique using stacked Support Vector Machine (SVM) classifiers, and trained and tested with the FERET data set images has shown that, indeed, CNN- and COSFIRE-based features are complimentary as their combination reduces the error rate by more than 25%.},
keywords = {brain-inspired, convnets, deep learning, face analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Automatic age group classification is the ability of an algorithm to classify face images into predetermined age groups. It is an important task due to its numerous applications such as monitoring, biometrics and commercial profiling. In this work we propose a fusion technique that combines CNN- and COSFIRE-based features for the recognition of age groups from face images. Both CNN and COSFIRE are trainable approaches that have been demonstrated to be effective in various computer vision applications. As to CNN, we use the pre-trained VGG-Face architecture and for COSFIRE we configure new COSFIRE filters from training data. Since recent literature suggests that CNNs deliver the highest accuracy rates within such problems, the hypothesis which we want to investigate in this work is whether combining CNN and COSFIRE approaches together will improve results. The proposed fusion technique using stacked Support Vector Machine (SVM) classifiers, and trained and tested with the FERET data set images has shown that, indeed, CNN- and COSFIRE-based features are complimentary as their combination reduces the error rate by more than 25%.
4.
Simanjuntak, Frans; Azzopardi, George
Fusion of CNN-and COSFIRE-Based Features with Application to Gender Recognition from Face Images Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, convnets, deep learning, face analysis, trainable filters
@inproceedings{simanjuntak2019fusion,
title = {Fusion of CNN-and COSFIRE-Based Features with Application to Gender Recognition from Face Images},
author = {Frans Simanjuntak and George Azzopardi},
doi = {https://doi.org/10.1007/978-3-030-17795-9_33},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {Science and Information Conference},
pages = {444--458},
organization = {Springer},
abstract = {Convolution neural networks (CNNs) have been demonstrated to be very eective in various computer vision tasks. The main strength of such networks is that features are learned from some training data. In cases where training data is not abundant, transfer learning can be used in order to adapt features that are pre-trained from other tasks. Similarly, the COSFIRE approach is also trainable as it configures lters to be selective for features selected from training data. In this study we propose a fusion method of these two approaches and evaluate their performance on the application of gender recognition from face images. In particular, we use the pre-trained VGGFace CNN, which when used as standalone, it achieved 97.45% on the GENDER-FERET data set. With one of the proposed fusion approaches the recognition rate on the same task is improved to 98.9%, that is reducing the error rate by more than 50%. Our experiments demonstrate that COSFIRE filters can provide complementary features to CNNs, which contribute to a better performance.},
keywords = {brain-inspired, convnets, deep learning, face analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Convolution neural networks (CNNs) have been demonstrated to be very eective in various computer vision tasks. The main strength of such networks is that features are learned from some training data. In cases where training data is not abundant, transfer learning can be used in order to adapt features that are pre-trained from other tasks. Similarly, the COSFIRE approach is also trainable as it configures lters to be selective for features selected from training data. In this study we propose a fusion method of these two approaches and evaluate their performance on the application of gender recognition from face images. In particular, we use the pre-trained VGGFace CNN, which when used as standalone, it achieved 97.45% on the GENDER-FERET data set. With one of the proposed fusion approaches the recognition rate on the same task is improved to 98.9%, that is reducing the error rate by more than 50%. Our experiments demonstrate that COSFIRE filters can provide complementary features to CNNs, which contribute to a better performance.
2018
5.
Azzopardi, George; Foggia, Pasquale; Greco, Antonio; Saggese, Alessia; Vento, Mario
Gender recognition from face images using trainable shape and color features Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, face analysis, trainable filters
@inproceedings{azzopardi2018gender,
title = {Gender recognition from face images using trainable shape and color features},
author = {George Azzopardi and Pasquale Foggia and Antonio Greco and Alessia Saggese and Mario Vento},
doi = {10.1109/ICPR.2018.8545771},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 24th International Conference on Pattern Recognition (ICPR)},
pages = {1983-1988},
organization = {IEEE},
abstract = {Gender recognition from face images is an important application and it is still an open computer vision problem, even though it is something trivial from the human visual system. Variations in pose, lighting, and expression are few of the problems that make such an application challenging for a computer system. Neurophysiological studies demonstrate that the human brain is able to distinguish men and women also in absence of external cues, by analyzing the shape of specific parts of the face. In this paper, we describe an automatic procedure that combines trainable shape and color features for gender classification. In particular the proposed method fuses edge-based and color-blob-based features by means of trainable COSFIRE filters. The former types of feature are able to extract information about the shape of a face whereas the latter extract information about shades of colors in different parts of the face. We use these two sets of features to create a stacked classification SVM model and demonstrate its effectiveness on the GENDER-COLOR-FERET dataset, where we achieve an accuracy of 96.4%.},
keywords = {brain-inspired, face analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Gender recognition from face images is an important application and it is still an open computer vision problem, even though it is something trivial from the human visual system. Variations in pose, lighting, and expression are few of the problems that make such an application challenging for a computer system. Neurophysiological studies demonstrate that the human brain is able to distinguish men and women also in absence of external cues, by analyzing the shape of specific parts of the face. In this paper, we describe an automatic procedure that combines trainable shape and color features for gender classification. In particular the proposed method fuses edge-based and color-blob-based features by means of trainable COSFIRE filters. The former types of feature are able to extract information about the shape of a face whereas the latter extract information about shades of colors in different parts of the face. We use these two sets of features to create a stacked classification SVM model and demonstrate its effectiveness on the GENDER-COLOR-FERET dataset, where we achieve an accuracy of 96.4%.
2017
6.
Azzopardi, George; Greco, Antonio; Saggese, Alessia; Vento, Mario
Fast gender recognition in videos using a novel descriptor based on the gradient magnitudes of facial landmarks Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: face analysis
@inproceedings{azzopardi2017fast,
title = {Fast gender recognition in videos using a novel descriptor based on the gradient magnitudes of facial landmarks},
author = {George Azzopardi and Antonio Greco and Alessia Saggese and Mario Vento},
doi = {10.1109/AVSS.2017.8078525},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
pages = {1--6},
organization = {IEEE},
abstract = {The growing interest in recent years for gender recognition from face images is mainly attributable to the wide range of possible applications that can be used for commercial and marketing purposes. It is desirable that such algorithms process high resolution video frames acquired by using surveillance cameras in real-time. To the best of our knowledge, however, there are no studies which analyze the computational impact of the methods and the difficulties related to the processing of faces extracted from videos captured in the wild. We propose a novel face descriptor based on the gradient magnitudes of facial landmarks, which are points automatically extracted from the face contour, eyes, eyebrows, nose, mouth and chin. We evaluate the effectiveness and efficiency of the proposed approach on two new datasets, which we made available online and that consist of color face images and color video sequences acquired in real scenarios. The proposed approach is more efficient and effective than three commercial libraries.},
keywords = {face analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The growing interest in recent years for gender recognition from face images is mainly attributable to the wide range of possible applications that can be used for commercial and marketing purposes. It is desirable that such algorithms process high resolution video frames acquired by using surveillance cameras in real-time. To the best of our knowledge, however, there are no studies which analyze the computational impact of the methods and the difficulties related to the processing of faces extracted from videos captured in the wild. We propose a novel face descriptor based on the gradient magnitudes of facial landmarks, which are points automatically extracted from the face contour, eyes, eyebrows, nose, mouth and chin. We evaluate the effectiveness and efficiency of the proposed approach on two new datasets, which we made available online and that consist of color face images and color video sequences acquired in real scenarios. The proposed approach is more efficient and effective than three commercial libraries.
2016
7.
Azzopardi, George; Greco, Antonio; Vento, Mario
Gender recognition from face images using a fusion of svm classifiers Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: face analysis
@inproceedings{azzopardi2016gender,
title = {Gender recognition from face images using a fusion of svm classifiers},
author = {George Azzopardi and Antonio Greco and Mario Vento},
doi = {https://doi.org/10.1007/978-3-319-41501-7_59},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {International Conference on Image Analysis and Recognition},
pages = {533--538},
organization = {Springer, 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 = {face analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
8.
Azzopardi, George; Greco, Antonio; Vento, Mario
Gender recognition from face images with trainable COSFIRE filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, face analysis, trainable filters
@inproceedings{azzopardi2016genderb,
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 = {2016-01-01},
urldate = {2016-01-01},
booktitle = {13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
pages = {235--241},
organization = {IEEE},
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 = {brain-inspired, face analysis, trainable filters},
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.