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Displaying 1-9 out of 9 total
Face spoofing detection from single images using micro-texture analysis
Found in: Biometrics, International Joint Conference on
By Jukka Maatta,Abdenour Hadid,Matti Pietikainen
Issue Date:October 2011
pp. 1-7
Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterization...
Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Masashi Nishiyama, Abdenour Hadid, Hidenori Takeshima, Jamie Shotton, Tatsuo Kozakaya, Osamu Yamaguchi
Issue Date:April 2011
pp. 838-845
This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images. The main issue is how to infer a Point Spread Function (PSF) representing the process of blur on faces. Inferring a PSF from a single facial image ...
Recognition of Blurred Faces via Facial Deblurring Combined with Blur-Tolerant Descriptors
Found in: Pattern Recognition, International Conference on
By Abdenour Hadid, Masashi Nishiyama, Yoichi Sato
Issue Date:August 2010
pp. 1160-1163
Blur is often present in real-world images and significantly affects the performance of face recognition systems. To improve the recognition of blurred faces, we propose a new approach which inherits the advantages of two recent methods. The idea consists ...
Face Description with Local Binary Patterns: Application to Face Recognition
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Timo Ahonen, Abdenour Hadid, Matti Pietikäinen
Issue Date:December 2006
pp. 2037-2041
This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhan...
A Hybrid Approach to Face Detection under Unconstrained Environments
Found in: Pattern Recognition, International Conference on
By Abdenour Hadid, Matti Pietikainen
Issue Date:August 2006
pp. 227-230
To detect faces in natural and unconstrained environments, we propose an approach which combines the advantages of both color and gray scale based methods. The idea consists of first preprocessing the images using a state-ofthe- art approach for skin model...
Face Recognition Based on the Appearance of Local Regions
Found in: Pattern Recognition, International Conference on
By Timo Ahonen, Matti Pietikäinen, Abdenour Hadid, Topi Mäenpää
Issue Date:August 2004
pp. 153-156
Recently, we proposed a novel facial representation for face recognition based on Local Binary Pattern (LBP) features. We obtained excellent results when dividing the face images into several regions from which the LBP features are extracted and concatenat...
Selecting Models from Videos for Appearance-Based Face Recognition
Found in: Pattern Recognition, International Conference on
By Abdenour Hadid, Matti Pietikäinen
Issue Date:August 2004
pp. 304-308
In this paper, we propose an unsupervised approach to select representative face samples (models) from raw videos and build an appearance-based face recognition system. The approach is based on representing the face manifold in a low-dimensional space usin...
A Discriminative Feature Space for Detecting and Recognizing Faces
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Abdenour Hadid, Matti Pietikäinen, Timo Ahonen
Issue Date:July 2004
pp. 797-804
In this paper, we introduce a novel discriminative feature space which is efficient not only for face detection but also for recognition. The face representation is based on local binary patterns (LBP) and consists of encoding both local and global facial ...
Learning local image descriptors using binary decision trees
Found in: 2014 IEEE Winter Conference on Applications of Computer Vision (WACV)
By Juha Ylioinas,Juho Kannala,Abdenour Hadid,Matti Pietikainen
Issue Date:March 2014
pp. 347-354
In this paper we propose a unified framework for learning such local image descriptors that describe pixel neighborhoods using binary codes. The descriptors are constructed using binary decision trees which are learnt from a set of training image patches. ...