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The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. The identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware...
During a forensic interview, high-stakes deception is very prevalent notwithstanding the heavy consequences that might result. This paper proposes an automated computer vision solution for detecting high-stakes deception based on facial clues. Four deceptive cues (eye-blink, eyebrow motion, wrinkle occurrence and mouth motion) were identified and integrated into a single facial behavior pattern vector...
Due to the simplicity and firm mathematical foundation, Support Vector Machines (SVMs) have been intensively used to solve classification problems. However, training SVMs on real world large-scale databases is computationally costly and sometimes infeasible when the dataset size is massive and non-stationary. In this paper, we propose an incremental learning approach that greatly reduces the time...
In this study, we are interested in the classification of short-duration sounds related to surveillance context. We carefully select a set of features allowing a better discrimination of the signals. Considering each pattern vector, we introduce the mean and standard deviation of every feature components. We also explore the way the signal is more appropriately analyzed by considering possible partitioning...
In this paper we present a novel approach to face recognition. We propose an adaptation and extension to the state-of-the-art methods in face recognition, such as sparse representation-based classification and its extensions. Effectively, our method combines the sparsity-based approaches with additional least-squares steps and exhitbits robustness to outliers achieving significant performance improvement...
In this paper, a computationally efficient approach to transcription of monophonic melodies from a raw acoustic signal is presented. Two different instance-based pitch classification methods are proposed, the choice of which depends on the size of the available training database. In the first method, the conventional K-Nearest Neighbor algorithm is trained on a large database of piano tones and employed...
This paper is concerned with novel features for fingerprint classification based on the Euclidian distance between the center point and their nearest neighbor bifurcation minutia's. The main advantage of the new method is the dimension reduction of the features vectors used to characterize fingerprint, compared with the classic characterization method based on the relative position of bifurcation...
On one hand, sparse coding, which is widely used in signal processing, consists of representing signals as linear combinations of few elementary patterns selected from a dedicated dictionary. The output is a sparse vector containing few coding coefficients and is called sparse code. On the other hand, Multilayer Perceptron (MLP) is a neural network classification method that learns non linear borders...
In this paper, a Sparse Representation based Classification (SRC) approach is employed for mine hunting using Synthetic Aperture Sonar (SAS) images. Given a training database with enough samples, SRC exploits the properties of sparse signals and expresses a sample of unknown class as a sparse linear combination of the training samples. The class of the training samples with greater weight is likely...
This paper presents a simple yet efficient face recognition technique, where a feature extraction algorithm is proposed based on the principle of spectral domain cross-correlation. Instead of considering the spatial variation of a face image as a whole, first we concentrate on spectral variation of each row of the image individually, which is obtained using discrete cosine transform (DCT). As each...
In automotive industry the safety of cars behavior is monitoring using computers. The information acquired on the bus communication is often redundant and not relevant. Therefore in the case of faults detection and isolation based on machine learning model, we need to reduce the number of variables according with their relevance and allowing taking decision in real time. In this paper, we propose...
This paper aims at classifying changed from unchanged pattern in multi-acquisition data using kernel based support vector data description (SVDD). Indeed, SVDD is a well known method allowing to map the data into a high dimensional features space where an hypersphere encloses most patterns belonging to the ”un-changed” class. In this work, we propose a new kernel function which combines the characteristics...
The filing and classification of E-government document in E-government information system are accomplished manually, which restricts the government information technology seriously. To solve this problem, a classification algorithm for E-government document based on support vector machine is proposed. For the general workflow of E-government document circulation in the current information system,...
Neighborhood coding was proposed to encode binary images. Previously, this coding scheme presented good results in the problem of handwritten character recognition. In this article, we extended this coding scheme so that it can be applied as an image shape descriptor and in a bilevel image compression method. An algorithm to reduce the number of codes needed to reconstruct the image without loss of...
A new method for texture classification is presented. The proposed method uses only 3 circular filters. Images are first filtered using these filters, then thresholded and averaged over two small neighborhoods. Universal textons are generated without learning from the training sets. 80 universal textons are used for each neighborhood. The feature space is reduced in one neighborhood by grouping into...
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