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Abdenour Hadid
IEMN DOAE, UMR CNRS 8520, Polytechnic University Hauts-de-France, 59300 Valenciennes, France

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Journal article
Published: 11 August 2021 in Electronics
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Automatic pain recognition from facial expressions is a challenging problem that has attracted a significant attention from the research community. This article provides a comprehensive analysis on the topic by comparing some popular and Off-the-Shell CNN (Convolutional Neural Network) architectures, including MobileNet, GoogleNet, ResNeXt-50, ResNet18, and DenseNet-161. We use these networks in two distinct modes: stand alone mode or feature extractor mode. In stand alone mode, the models (i.e., the networks) are used for directly estimating the pain. In feature extractor mode, the “values” of the middle layers are extracted and used as inputs to classifiers, such as SVR (Support Vector Regression) and RFR (Random Forest Regression). We perform extensive experiments on the benchmarking and publicly available database called UNBC-McMaster Shoulder Pain. The obtained results are interesting as they give valuable insights into the usefulness of the hidden CNN layers for automatic pain estimation.

ACS Style

Safaa El Morabit; Atika Rivenq; Mohammed-En-Nadhir Zighem; Abdenour Hadid; Abdeldjalil Ouahabi; Abdelmalik Taleb-Ahmed. Automatic Pain Estimation from Facial Expressions: A Comparative Analysis Using Off-the-Shelf CNN Architectures. Electronics 2021, 10, 1926 .

AMA Style

Safaa El Morabit, Atika Rivenq, Mohammed-En-Nadhir Zighem, Abdenour Hadid, Abdeldjalil Ouahabi, Abdelmalik Taleb-Ahmed. Automatic Pain Estimation from Facial Expressions: A Comparative Analysis Using Off-the-Shelf CNN Architectures. Electronics. 2021; 10 (16):1926.

Chicago/Turabian Style

Safaa El Morabit; Atika Rivenq; Mohammed-En-Nadhir Zighem; Abdenour Hadid; Abdeldjalil Ouahabi; Abdelmalik Taleb-Ahmed. 2021. "Automatic Pain Estimation from Facial Expressions: A Comparative Analysis Using Off-the-Shelf CNN Architectures." Electronics 10, no. 16: 1926.

Journal article
Published: 09 March 2021 in Journal of Imaging
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In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

ACS Style

Emanuela Paladini; Edoardo Vantaggiato; Fares Bougourzi; Cosimo Distante; Abdenour Hadid; Abdelmalik Taleb-Ahmed. Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification. Journal of Imaging 2021, 7, 51 .

AMA Style

Emanuela Paladini, Edoardo Vantaggiato, Fares Bougourzi, Cosimo Distante, Abdenour Hadid, Abdelmalik Taleb-Ahmed. Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification. Journal of Imaging. 2021; 7 (3):51.

Chicago/Turabian Style

Emanuela Paladini; Edoardo Vantaggiato; Fares Bougourzi; Cosimo Distante; Abdenour Hadid; Abdelmalik Taleb-Ahmed. 2021. "Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification." Journal of Imaging 7, no. 3: 51.

Journal article
Published: 03 March 2021 in Sensors
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The recognition of COVID-19 infection from X-ray images is an emerging field in the learning and computer vision community. Despite the great efforts that have been made in this field since the appearance of COVID-19 (2019), the field still suffers from two drawbacks. First, the number of available X-ray scans labeled as COVID-19-infected is relatively small. Second, all the works that have been carried out in the field are separate; there are no unified data, classes, and evaluation protocols. In this work, based on public and newly collected data, we propose two X-ray COVID-19 databases, which are three-class COVID-19 and five-class COVID-19 datasets. For both databases, we evaluate different deep learning architectures. Moreover, we propose an Ensemble-CNNs approach which outperforms the deep learning architectures and shows promising results in both databases. In other words, our proposed Ensemble-CNNs achieved a high performance in the recognition of COVID-19 infection, resulting in accuracies of 100% and 98.1% in the three-class and five-class scenarios, respectively. In addition, our approach achieved promising results in the overall recognition accuracy of 75.23% and 81.0% for the three-class and five-class scenarios, respectively. We make our databases of COVID-19 X-ray scans publicly available to encourage other researchers to use it as a benchmark for their studies and comparisons.

ACS Style

Edoardo Vantaggiato; Emanuela Paladini; Fares Bougourzi; Cosimo Distante; Abdenour Hadid; Abdelmalik Taleb-Ahmed. COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases. Sensors 2021, 21, 1742 .

AMA Style

Edoardo Vantaggiato, Emanuela Paladini, Fares Bougourzi, Cosimo Distante, Abdenour Hadid, Abdelmalik Taleb-Ahmed. COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases. Sensors. 2021; 21 (5):1742.

Chicago/Turabian Style

Edoardo Vantaggiato; Emanuela Paladini; Fares Bougourzi; Cosimo Distante; Abdenour Hadid; Abdelmalik Taleb-Ahmed. 2021. "COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases." Sensors 21, no. 5: 1742.

Journal article
Published: 25 January 2019 in IEEE Transactions on Information Forensics and Security
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ACS Style

Lei Li; Zhaoqiang Xia; Abdenour Hadid; Xiaoyue Jiang; Haixi Zhang; Xiaoyi Feng. Replayed Video Attack Detection Based on Motion Blur Analysis. IEEE Transactions on Information Forensics and Security 2019, 14, 2246 -2261.

AMA Style

Lei Li, Zhaoqiang Xia, Abdenour Hadid, Xiaoyue Jiang, Haixi Zhang, Xiaoyi Feng. Replayed Video Attack Detection Based on Motion Blur Analysis. IEEE Transactions on Information Forensics and Security. 2019; 14 (9):2246-2261.

Chicago/Turabian Style

Lei Li; Zhaoqiang Xia; Abdenour Hadid; Xiaoyue Jiang; Haixi Zhang; Xiaoyi Feng. 2019. "Replayed Video Attack Detection Based on Motion Blur Analysis." IEEE Transactions on Information Forensics and Security 14, no. 9: 2246-2261.

Journal article
Published: 01 July 2018 in Journal of Visual Communication and Image Representation
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Nowadays, face biometric based access control systems are becoming ubiquitous in our daily life while they are still vulnerable to spoofing attacks. So developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning based detection methods cannot be updated to optimum due to limited data. Local Binary Pattern (LBP), effective features for face recognition, have been employed in face spoofing detection and obtained promising results. Considering the similarities between LBP extraction and convolutional neural network (CNN) that the former can be accomplished by using fixed convolutional filters, we propose a novel end-to-end learnable LBP network for face spoofing detection. Our network can significantly reduce the number of network parameters by combing learnable convolutional layers with fixed-parameter LBP layers that are comprised of sparse binary filters and derivable simulated gate functions. Compared with existing deep leaning based detection methods, the parameters in our fully connected layers are up to 64x64x savings. Conducting extensive experiments on two standard spoofing databases, i.e., Relay-Attack and CASIA-FA, our proposed LBP network substantially outperforms the state-of-the-art methods.

ACS Style

Lei Li; Xiaoyi Feng; Zhaoqiang Xia; Xiaoyue Jiang; Abdenour Hadid. Face spoofing detection with local binary pattern network. Journal of Visual Communication and Image Representation 2018, 54, 182 -192.

AMA Style

Lei Li, Xiaoyi Feng, Zhaoqiang Xia, Xiaoyue Jiang, Abdenour Hadid. Face spoofing detection with local binary pattern network. Journal of Visual Communication and Image Representation. 2018; 54 ():182-192.

Chicago/Turabian Style

Lei Li; Xiaoyi Feng; Zhaoqiang Xia; Xiaoyue Jiang; Abdenour Hadid. 2018. "Face spoofing detection with local binary pattern network." Journal of Visual Communication and Image Representation 54, no. : 182-192.

Journal article
Published: 16 November 2017 in IET Biometrics
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Among tangible threats facing current biometric systems are spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby attempting to gain illegitimate access and advantages. Recently, an increasing attention has been given to this research problem, as can be attested by the growing number of articles and the various competitions that appear in major biometric forums. This study presents a comprehensive overview of the recent advances in face anti-spoofing state-of-the-art, discussing existing methodologies, available benchmarking databases, reported results and, more importantly, the open issues and future research directions. As a case study for illustration, a face anti-spoofing method is described, which employs a colour local binary pattern descriptor to jointly analyse colour and texture available from the luminance and chrominance channels. Two publicly available databases are used for the analysis, and the importance of inter-database evaluation to attest the generalisation capabilities of an anti-spoofing method is discussed.

ACS Style

Lei Li; Paulo Correia; Abdenour Hadid. Face recognition under spoofing attacks: countermeasures and research directions. IET Biometrics 2017, 7, 3 -14.

AMA Style

Lei Li, Paulo Correia, Abdenour Hadid. Face recognition under spoofing attacks: countermeasures and research directions. IET Biometrics. 2017; 7 (1):3-14.

Chicago/Turabian Style

Lei Li; Paulo Correia; Abdenour Hadid. 2017. "Face recognition under spoofing attacks: countermeasures and research directions." IET Biometrics 7, no. 1: 3-14.

Journal article
Published: 07 November 2017 in IET Biometrics
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The recent significant progress in face recognition is mainly achieved using learning-based (LE) techniques via an exhaustive training involving a huge number of face samples. However, in many applications, the number of face images available for training may be very limited. This makes LE techniques impractical for learning discriminative features and models. Thus, limited number of face samples (i.e. scarce data) degrades the recognition performance of most existing methods. To overcome this problem, the authors propose a novel approach based on two-layer collaborative representation to exploit the abundance of samples in some classes to enrich the scarce data in other classes. The first-layer collaborative representation uses the abundance of samples to construct representations for the scarce data. Then, a new face sample is recognised by computing residuals with the second-layer collaborative representation. Extensive experiments on four benchmark face databases demonstrate the effectiveness of their proposed approach which compares favourably against state-of-the-art methods.

ACS Style

Zhaoqiang Xia; Xianlin Peng; Xiaoyi Feng; Abdenour Hadid. Scarce face recognition via two‐layer collaborative representation. IET Biometrics 2017, 7, 56 -62.

AMA Style

Zhaoqiang Xia, Xianlin Peng, Xiaoyi Feng, Abdenour Hadid. Scarce face recognition via two‐layer collaborative representation. IET Biometrics. 2017; 7 (1):56-62.

Chicago/Turabian Style

Zhaoqiang Xia; Xianlin Peng; Xiaoyi Feng; Abdenour Hadid. 2017. "Scarce face recognition via two‐layer collaborative representation." IET Biometrics 7, no. 1: 56-62.

Journal article
Published: 01 November 2017 in Signal Processing: Image Communication
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ACS Style

Zhaoqiang Xia; Xiaoyi Feng; Jie Lin; Abdenour Hadid. Deep convolutional hashing using pairwise multi-label supervision for large-scale visual search. Signal Processing: Image Communication 2017, 59, 109 -116.

AMA Style

Zhaoqiang Xia, Xiaoyi Feng, Jie Lin, Abdenour Hadid. Deep convolutional hashing using pairwise multi-label supervision for large-scale visual search. Signal Processing: Image Communication. 2017; 59 ():109-116.

Chicago/Turabian Style

Zhaoqiang Xia; Xiaoyi Feng; Jie Lin; Abdenour Hadid. 2017. "Deep convolutional hashing using pairwise multi-label supervision for large-scale visual search." Signal Processing: Image Communication 59, no. : 109-116.

Journal article
Published: 05 October 2017 in IEEE Journal of Biomedical and Health Informatics
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Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patients condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives such as the need for interdisciplinary collaboration and collecting publicly available databases are highlighted.

ACS Style

Jerome Thevenot; Miguel Bordallo Lopez; Abdenour Hadid. A Survey on Computer Vision for Assistive Medical Diagnosis From Faces. IEEE Journal of Biomedical and Health Informatics 2017, 22, 1497 -1511.

AMA Style

Jerome Thevenot, Miguel Bordallo Lopez, Abdenour Hadid. A Survey on Computer Vision for Assistive Medical Diagnosis From Faces. IEEE Journal of Biomedical and Health Informatics. 2017; 22 (5):1497-1511.

Chicago/Turabian Style

Jerome Thevenot; Miguel Bordallo Lopez; Abdenour Hadid. 2017. "A Survey on Computer Vision for Assistive Medical Diagnosis From Faces." IEEE Journal of Biomedical and Health Informatics 22, no. 5: 1497-1511.

Preprint
Published: 14 August 2017
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Automatic kinship verification using facial images is a relatively new and challenging research problem in computer vision. It consists in automatically predicting whether two persons have a biological kin relation by examining their facial attributes. While most of the existing works extract shallow handcrafted features from still face images, we approach this problem from spatio-temporal point of view and explore the use of both shallow texture features and deep features for characterizing faces. Promising results, especially those of deep features, are obtained on the benchmark UvA-NEMO Smile database. Our extensive experiments also show the superiority of using videos over still images, hence pointing out the important role of facial dynamics in kinship verification. Furthermore, the fusion of the two types of features (i.e. shallow spatio-temporal texture features and deep features) shows significant performance improvements compared to state-of-the-art methods.

ACS Style

Elhocine Boutellaa; Miguel Bordallo Lopez; Samy Ait-Aoudia; Xiaoyi Feng; Abdenour Hadid. Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning. 2017, 1 .

AMA Style

Elhocine Boutellaa, Miguel Bordallo Lopez, Samy Ait-Aoudia, Xiaoyi Feng, Abdenour Hadid. Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning. . 2017; ():1.

Chicago/Turabian Style

Elhocine Boutellaa; Miguel Bordallo Lopez; Samy Ait-Aoudia; Xiaoyi Feng; Abdenour Hadid. 2017. "Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning." , no. : 1.

Journal article
Published: 01 August 2017 in Computers & Electrical Engineering
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ACS Style

Abdelmalik Ouamane; Elhocine Boutellaa; Messaoud Bengherabi; Abdenour Hadid; Abdelmalik Taleb-Ahmed. A novel statistical and multiscale local binary feature for 2D and 3D face verification. Computers & Electrical Engineering 2017, 62, 68 -80.

AMA Style

Abdelmalik Ouamane, Elhocine Boutellaa, Messaoud Bengherabi, Abdenour Hadid, Abdelmalik Taleb-Ahmed. A novel statistical and multiscale local binary feature for 2D and 3D face verification. Computers & Electrical Engineering. 2017; 62 ():68-80.

Chicago/Turabian Style

Abdelmalik Ouamane; Elhocine Boutellaa; Messaoud Bengherabi; Abdenour Hadid; Abdelmalik Taleb-Ahmed. 2017. "A novel statistical and multiscale local binary feature for 2D and 3D face verification." Computers & Electrical Engineering 62, no. : 68-80.

Journal article
Published: 21 June 2017 in IEEE Transactions on Information Forensics and Security
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We propose a novel approach for face verification by encoding 2D and 3D face images as a high order tensor. To perform tensor dimensionality reduction for both the unsupervised and supervised cases, we propose Multilinear Whitened Principal Component Analysis (MWPCA) and Tensor Exponential Discriminant Analysis (TEDA), respectively. MWPCA is utilized to solve the Small Sample Size (SSS) problem in the high-dimensional space and to improve the discrimination power achieved by classical Multilinear Principal Component Analysis (MPCA). In the supervised case, we extend Multilinear Discriminant Analysis (MDA) to TEDA in order to emphasize the discriminant data included in the null space of the within-class scatter matrix of each tensor’s mode. Additionally, TEDA enlarges the margin between samples belonging to different classes via distance diffusion mappings. Our proposed approach can be seen as a novel data fusion method based on tensor representation. Indeed, the histograms of different local descriptors extracted from both 2D and 3D face modalities are combined through different tensor modes. The extensive experimental evaluation carried out on FRGC v2.0, Bosphorus and CASIA 2D and 3D face databases indicates that the proposed approach performs significantly better than state-of-the-art approaches.

ACS Style

Abdelmalik Ouamane; Ammar Chouchane; Elhocine Boutellaa; Mebarka Belahcene; Salah Bourennane; Abdenour Hadid. Efficient Tensor-Based 2D+3D Face Verification. IEEE Transactions on Information Forensics and Security 2017, 12, 2751 -2762.

AMA Style

Abdelmalik Ouamane, Ammar Chouchane, Elhocine Boutellaa, Mebarka Belahcene, Salah Bourennane, Abdenour Hadid. Efficient Tensor-Based 2D+3D Face Verification. IEEE Transactions on Information Forensics and Security. 2017; 12 (11):2751-2762.

Chicago/Turabian Style

Abdelmalik Ouamane; Ammar Chouchane; Elhocine Boutellaa; Mebarka Belahcene; Salah Bourennane; Abdenour Hadid. 2017. "Efficient Tensor-Based 2D+3D Face Verification." IEEE Transactions on Information Forensics and Security 12, no. 11: 2751-2762.

Journal article
Published: 15 June 2017 in IEEE MultiMedia
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To address the issues like identity theft and security threats, a continuously evolving technology known as biometrics is presently being deployed in a wide range of personal, government, and commercial applications. Despite the great progress in the field, several exigent problems have yet to be addressed to unleash biometrics full potential. This article aims to present an overview of biometric research and more importantly the significant progress that has been attained over the recent years. The paper is envisaged to further not only the understanding of general audiences and policy makers but also interdisciplinary research. Most importantly, this article is intended to complement earlier articles with updates on most recent topics and developments related to e.g. spoofing, evasion, obfuscation, face reconstruction from DNA, Big data issues in biometrics, etc.

ACS Style

Zahid Akhtar; Abdenour Hadid; Mark Nixon; Massimo Tistarelli; Jean-Luc Dugelay; Sebastien Marcel. Biometrics: In Search of Identity and Security (Q & A). IEEE MultiMedia 2017, 1 -1.

AMA Style

Zahid Akhtar, Abdenour Hadid, Mark Nixon, Massimo Tistarelli, Jean-Luc Dugelay, Sebastien Marcel. Biometrics: In Search of Identity and Security (Q & A). IEEE MultiMedia. 2017; (99):1-1.

Chicago/Turabian Style

Zahid Akhtar; Abdenour Hadid; Mark Nixon; Massimo Tistarelli; Jean-Luc Dugelay; Sebastien Marcel. 2017. "Biometrics: In Search of Identity and Security (Q & A)." IEEE MultiMedia , no. 99: 1-1.

Conference paper
Published: 01 December 2016 in 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
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Learning based hashing plays a pivotal role in large-scale visual search. However, most existing hashing algorithms tend to learn shallow models that do not seek representative binary codes. In this paper, we propose a novel hashing approach based on unsupervised deep learning to hierarchically transform features into hash codes. Within the heterogeneous deep hashing framework, the autoencoder layers with specific constraints are considered to model the nonlinear mapping between features and binary codes. Then, a Restricted Boltzmann Machine (RBM) layer with constraints is utilized to reduce the dimension in the hamming space. The experiments on the problem of visual search demonstrate the competitiveness of our proposed approach compared to the state of the art.

ACS Style

Zhaoqiang Xia; Xiaoyi Feng; Jinye Peng; Abdenour Hadid. Unsupervised deep hashing for large-scale visual search. 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) 2016, 1 -5.

AMA Style

Zhaoqiang Xia, Xiaoyi Feng, Jinye Peng, Abdenour Hadid. Unsupervised deep hashing for large-scale visual search. 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). 2016; ():1-5.

Chicago/Turabian Style

Zhaoqiang Xia; Xiaoyi Feng; Jinye Peng; Abdenour Hadid. 2016. "Unsupervised deep hashing for large-scale visual search." 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) , no. : 1-5.

Conference paper
Published: 01 December 2016 in 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
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Text in images and videos is vital for understanding the visual content. In this paper, we propose to combine different features (namely corner, stroke width similarity and color similarity) to detect Chinese text in complex images and videos. The corners are used to determine the potential text candidates which are then refined using stroke width and color features. To further enhance the efficiency of the detection algorithm, a line scanning strategy is adopted to select the correct text regions. A new challenging data set with ground truth and evaluation protocol is built and will be made publicly available for research purposes. It is collected from different TV programs. Extensive experimental analysis shows that our proposed algorithm yields in very promising results which compare favorably against traditional approaches in the research literature.

ACS Style

Xiaoyue Jiang; Jie Lian; Zhaoqiang Xia; Xiaoyi Feng; Abdenour Hadid. Fast Chinese character detection from complex scenes. 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) 2016, 1 -4.

AMA Style

Xiaoyue Jiang, Jie Lian, Zhaoqiang Xia, Xiaoyi Feng, Abdenour Hadid. Fast Chinese character detection from complex scenes. 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). 2016; ():1-4.

Chicago/Turabian Style

Xiaoyue Jiang; Jie Lian; Zhaoqiang Xia; Xiaoyi Feng; Abdenour Hadid. 2016. "Fast Chinese character detection from complex scenes." 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) , no. : 1-4.

Journal article
Published: 18 November 2016 in IEEE Signal Processing Letters
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The vulnerabilities of face biometric authentication systems to spoofing attacks have received a significant attention during the recent years. Some of the proposed countermeasures have achieved impressive results when evaluated on intratests, i.e., the system is trained and tested on the same database. Unfortunately, most of these techniques fail to generalize well to unseen attacks, e.g., when the system is trained on one database and then evaluated on another database. This is a major concern in biometric antispoofing research that is mostly overlooked. In this letter, we propose a novel solution based on describing the facial appearance by applying Fisher vector encoding on speeded-up robust features extracted from different color spaces. The evaluation of our countermeasure on three challenging benchmark face-spoofing databases, namely the CASIA face antispoofing database, the replay-attack database, and MSU mobile face spoof database, showed excellent and stable performance across all the three datasets. Most importantly, in interdatabase tests, our proposed approach outperforms the state of the art and yields very promising generalization capabilities, even when only limited training data are used.

ACS Style

Zinelabidine Boulkenafet; Jukka Komulainen; Abdenour Hadid. Face Anti-Spoofing using Speeded-Up Robust Features and Fisher Vector Encoding. IEEE Signal Processing Letters 2016, 24, 1 -1.

AMA Style

Zinelabidine Boulkenafet, Jukka Komulainen, Abdenour Hadid. Face Anti-Spoofing using Speeded-Up Robust Features and Fisher Vector Encoding. IEEE Signal Processing Letters. 2016; 24 (2):1-1.

Chicago/Turabian Style

Zinelabidine Boulkenafet; Jukka Komulainen; Abdenour Hadid. 2016. "Face Anti-Spoofing using Speeded-Up Robust Features and Fisher Vector Encoding." IEEE Signal Processing Letters 24, no. 2: 1-1.

Conference paper
Published: 01 July 2016 in Lecture Notes in Computer Science
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The ability to automatically determine whether two persons are from the same family or not is referred to as Kinship (or family) verification. This is a recent and challenging research topic in computer vision. We propose in this paper a novel approach to kinship verification from facial images. Our solution uses similarity metric based convolutional neural networks. The system is trained using Siamese architecture specific constraints. Extensive experiments on the benchmark KinFaceW-I & II kinship face datasets showed promising results compared to many state-of-the-art methods.

ACS Style

Lei Li; Xiaoyi Feng; Xiaoting Wu; Zhaoqiang Xia; Abdenour Hadid. Kinship Verification from Faces via Similarity Metric Based Convolutional Neural Network. Lecture Notes in Computer Science 2016, 539 -548.

AMA Style

Lei Li, Xiaoyi Feng, Xiaoting Wu, Zhaoqiang Xia, Abdenour Hadid. Kinship Verification from Faces via Similarity Metric Based Convolutional Neural Network. Lecture Notes in Computer Science. 2016; ():539-548.

Chicago/Turabian Style

Lei Li; Xiaoyi Feng; Xiaoting Wu; Zhaoqiang Xia; Abdenour Hadid. 2016. "Kinship Verification from Faces via Similarity Metric Based Convolutional Neural Network." Lecture Notes in Computer Science , no. : 539-548.

Conference paper
Published: 01 June 2016 in 2016 International Conference on Biometrics (ICB)
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ACS Style

Elhocine Boutellaa; Miguel Bordallo Lopez; Samy Ait-Aoudia; Xiaoyi Feng; Abdenour Hadid. Notice of Removal: Kinship verification from videos using spatio-temporal texture features and deep learning. 2016 International Conference on Biometrics (ICB) 2016, 1 -7.

AMA Style

Elhocine Boutellaa, Miguel Bordallo Lopez, Samy Ait-Aoudia, Xiaoyi Feng, Abdenour Hadid. Notice of Removal: Kinship verification from videos using spatio-temporal texture features and deep learning. 2016 International Conference on Biometrics (ICB). 2016; ():1-7.

Chicago/Turabian Style

Elhocine Boutellaa; Miguel Bordallo Lopez; Samy Ait-Aoudia; Xiaoyi Feng; Abdenour Hadid. 2016. "Notice of Removal: Kinship verification from videos using spatio-temporal texture features and deep learning." 2016 International Conference on Biometrics (ICB) , no. : 1-7.

Journal article
Published: 20 April 2016 in IEEE Transactions on Information Forensics and Security
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Research on non-intrusive software-based face spoofing detection schemes has been mainly focused on the analysis of the luminance information of the face images, hence discarding the chroma component, which can be very useful for discriminating fake faces from genuine ones. This paper introduces a novel and appealing approach for detecting face spoofing using a colour texture analysis. We exploit the joint colour-texture information from the luminance and the chrominance channels by extracting complementary low-level feature descriptions from different colour spaces. More specifically, the feature histograms are computed over each image band separately. Extensive experiments on the three most challenging benchmark data sets, namely, the CASIA face anti-spoofing database, the replay-attack database, and the MSU mobile face spoof database, showed excellent results compared with the state of the art. More importantly, unlike most of the methods proposed in the literature, our proposed approach is able to achieve stable performance across all the three benchmark data sets. The promising results of our cross-database evaluation suggest that the facial colour texture representation is more stable in unknown conditions compared with its gray-scale counterparts.

ACS Style

Zinelabidine Boulkenafet; Jukka Komulainen; Abdenour Hadid. Face Spoofing Detection Using Colour Texture Analysis. IEEE Transactions on Information Forensics and Security 2016, 11, 1818 -1830.

AMA Style

Zinelabidine Boulkenafet, Jukka Komulainen, Abdenour Hadid. Face Spoofing Detection Using Colour Texture Analysis. IEEE Transactions on Information Forensics and Security. 2016; 11 (8):1818-1830.

Chicago/Turabian Style

Zinelabidine Boulkenafet; Jukka Komulainen; Abdenour Hadid. 2016. "Face Spoofing Detection Using Colour Texture Analysis." IEEE Transactions on Information Forensics and Security 11, no. 8: 1818-1830.

Journal article
Published: 01 December 2015 in Pattern Recognition Letters
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ACS Style

Elhocine Boutellaa; Abdenour Hadid; Messaoud Bengherabi; Samy Ait-Aoudia. On the use of Kinect depth data for identity, gender and ethnicity classification from facial images. Pattern Recognition Letters 2015, 68, 270 -277.

AMA Style

Elhocine Boutellaa, Abdenour Hadid, Messaoud Bengherabi, Samy Ait-Aoudia. On the use of Kinect depth data for identity, gender and ethnicity classification from facial images. Pattern Recognition Letters. 2015; 68 ():270-277.

Chicago/Turabian Style

Elhocine Boutellaa; Abdenour Hadid; Messaoud Bengherabi; Samy Ait-Aoudia. 2015. "On the use of Kinect depth data for identity, gender and ethnicity classification from facial images." Pattern Recognition Letters 68, no. : 270-277.