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Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions...
In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the observations. A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed...
In this paper, we present a novel scheme for text-independent online writer identification. As a first contribution, we propose histogram based features, inspired from the area of object detection, to describe the structural primitives of handwriting. Secondly, we have used sparse coding techniques to learn prototypes, that describe the general writing characteristics of the authors. To the best of...
Dictionary learning has been applied to computer vision problems such as facial expression recognition. K-SVD is one of the state-of-the-art dictionary learning algorithms. However, K-SVD is unsupervised and focuses only on the representational power. In this paper, we adopt label-consistent K-SVD with scattering transform in facial expression recognition. In addition to reducing the reconstruction...
Partially occluded or illuminated faces pose a significant obstacle for robust, real-world face recognition. The problem of how to characterize the error caused by occlusion or illumination is still a challenging task. There must exist some close relationship between the error metric and error distribution. However, some metric (e.g. Z2-norm) can't characterize this error distribution completely....
To search for a particular motion from a large database, a user-friendly and efficient retrieval mechanism is essential. In this paper, we propose a human motion retrieval system based on sparse coding and touch less interactions. Compared with existing methods that involve vector quantization, sparse coding leads to a more compact and discriminative representation. Motion comparison based on sparse...
The recent emerging sparse coding (SC) algorithms do not take local manifold structure of samples into consideration, while graph regularized sparse coding (GraphSC) algorithm only constrains the locality consistency of samples. Furthermore, the graph construction approach based on k-nearest-neighbor usually pre-defines the number of neighbors for all the samples, which may fails to fit the intrinsic...
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...
Availability of large amounts of raw unlabeled data has sparked the recent surge in semi-supervised learning research. In most works, however, it is assumed that labeled and unlabeled data come from the same distribution. This restriction is removed in the self-taught learning approach where unlabeled data can be different, but nevertheless have similar structure. First, a representation is learned...
In this paper, a robust feature for text-independent speaker recognition is proposed, which simulate the response mode of cochlear neurons in processing acoustic signal. The feature is derived from sparse coding coefficient which is computed on a learned over-complete dictionary, and the dictionary is considered similar to part of speech sensitive cochlear neurons. Furthermore, the feature is generated...
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