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Face spoofing detection nowadays has attracted attentions regarding the biometrics authentication issue. Inspired by the observation that face spoofing detection is highly relevant with the inherent image quality which also strongly depends on the properties of the capturing devices and conditions, in this paper, we tackle the spoofing detection problem based on a two-stage learning approach. Firstly,...
In this paper we propose a publicly available static hand pose database called OUHANDS and protocols for training and evaluating hand pose classification and hand detection methods. A comparison between the OUHANDS database and existing databases is given. Baseline results for both of the protocols are presented.
It is generally well known that the overall performance of the most widely used types of unsupervised change detection methods, based on the luminance pixel-wise difference, is mainly relied on the quality of the so-called difference image and the accuracy of the classification method. In order to address these two issues, this work proposes to first estimate, a new and robust similarity feature map,...
This paper presents our efforts towards a framework for video annotation using gaze. In computer vision, video annotation (VA) is an essential step in providing a ground truth for the evaluation of object detection and tracking techniques. VA is a demanding element in the development of video processing algorithms, where each object of interest should be manually labelled. Although the community has...
Multiple instance learning (MIL) is a form of weakly supervised learning for problems in which training instances are arranged into bags, and a label is provided for whole bags but not for individual instances. Most proposed MIL algorithms focus on bag classification, but more recently, the classification of individual instances has attracted the attention of the pattern recognition community. While...
Securing face recognition systems against replay attacks has been recognized as a real challenge. In this work, the problem of fake face detection is addressed by modelling radiometric distortions involved in the recapturing process. The originality of our approach is that the fake face detection process occurs after the face identification process. Having access to enrolment data of each client,...
Multi-spectral face recognition has been an active area of research over the past few decades. However, the vulnerability of multi-spectral face recognition systems is a growing concern that argues the need for Presentation Attack Detection (PAD) (or countermeasure or anti-spoofing) schemes to successfully detect targeted attacks. In this work, we present a novel feature descriptor LαMTiF that can...
We introduce a novel method of cell detection and segmentation based on a polar transformation. The method assumes that the seed point of each candidate is placed inside the nucleus. The polar representation, built around the seed, is segmented using k-means clustering into one candidate-nucleus cluster, one candidate-cytoplasm cluster and up to three miscellaneous clusters, representing background...
In this paper we propose a new method for segmenting retinal vessels in adaptive optics images. This method is particularly dedicated for segmenting vessels with significant morphological alterations due to vasculitis, but it is also accurate for vessels with moderate or without alteration. It relies on a pre-segmentation step which is crucial for the robustness and accuracy of the results. This step...
Head measurements are widely used in different fields as ergonomics, medicine and acoustics. In acoustics they are useful to create 3D virtual auditoriums, since the manipulation of the Head Related Transfer Function (HRTF) allows to virtually place sound sources. Despite the HRTF dependents on the physical characteristics of each person, a generic HRTF is frequently adopted in commercial systems...
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...
Algorithms for facial landmark detection in real-world images require manually annotated training databases. However, the task of selecting or creating the images and annotating the data is extremely time-consuming, leaving researchers with the options of investing significant amounts of time for creating annotated images optimized for the given task or resigning from creating such hand-labeled databases...
This paper suggests a new method for detecting 2D translation between two images based on calculating three independent cross-correlations (CCs) on them. Such a method is conceptually different from other area based methods which generally perform only one CC or its variants for phase shift detection. The principle of traditional area based methods could be interpreted as a fast but simplified implementation...
The Walsh-Hadamard transform plays a major role in many image and video coding algorithms. In one hand, its intensive use in these algorithms makes its acceleration a challenge, in order to speed-up the algorithm execution. On the other hand, the available fast implementations are not efficient across different platforms. In this work, a parallel-based implementation of the WHT is proposed for CPU...
In this paper, we present a novel method for human action recognition using covariance features. Computationally efficient action features are extracted from the skeleton of the subject performing the action. They aim to capture relative positions of the joints and motion over time. These features are encoded into a compact representation using a covariance matrix. We evaluate the performance of the...
Pedestrian detection has been used in applications such as car safety, video surveillance, and intelligent vehicles. In this paper, we present a pedestrian detection scheme using HOG, LUV and optical flow features with AdaBoost Decision Stump classifier. Our experiments on Caltech-USA pedestrian dataset show that the proposed scheme achieves promising results of about 16.7% log-average miss rate.
In this paper, we propose a mutual framework that combines two state-of-the-art visual object tracking algorithms. Both trackers benefit from each other's advantage leading to an efficient visual tracking approach. Many state-of-the-art trackers have poor performance due to rain, fog or occlusion in real-world scenarios. Often, after several frames, objects are getting lost, only leading to a short-term...
This paper approaches the problem of geometric multi-model fitting as a data segmentation problem which is solved by a sequence of sampling, model selection and clustering steps. We propose a sampling method that significantly facilitates solving the segmentation problem using the Normalized cut. The sampler is a novel application of Markov-Chain-Monte-Carlo (MCMC) method to sample from a distribution...
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