F.4. Probability and Statistics
Z. Shaeiri; M. R. Karami; A. Aghagolzadeh
Abstract
Sufficient number of linear and noisy measurements for exact and approximate sparsity pattern/support set recovery in the high dimensional setting is derived. Although this problem as been addressed in the recent literature, there is still considerable gaps between those results and the exact limits ...
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Sufficient number of linear and noisy measurements for exact and approximate sparsity pattern/support set recovery in the high dimensional setting is derived. Although this problem as been addressed in the recent literature, there is still considerable gaps between those results and the exact limits of the perfect support set recovery. To reduce this gap, in this paper, the sufficient condition is enhanced. A specific form of a Joint Typicality decoder is used for the support recovery task. Two performance metrics are considered for the recovery validation; one, which considers exact support recovery, and the other which seeks partial support recovery. First, an upper bound is obtained on the error probability of the sparsity pattern recovery. Next, using the mentioned upper bound, sufficient number of measurements for reliable support recovery is derived. It is shown that the sufficient condition for reliable support recovery depends on three key parameters of the problem; the noise variance, the minimum nonzero entry of the unknown sparse vector and the sparsity level. Simulations are performed for different sparsity rate, different noise variances, and different distortion levels. The results show that for all the mentioned cases the proposed methodology increases convergence rate of upper bound of the error probability of support recovery significantly which leads to a lower error probability bound compared with previously proposed bounds.
H.6.5.4. Face and gesture recognition
S. Shafeipour Yourdeshahi; H. Seyedarabi; A. Aghagolzadeh
Abstract
Video-based face recognition has attracted significant attention in many applications such as media technology, network security, human-machine interfaces, and automatic access control system in the past decade. The usual way for face recognition is based upon the grayscale image produced by combining ...
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Video-based face recognition has attracted significant attention in many applications such as media technology, network security, human-machine interfaces, and automatic access control system in the past decade. The usual way for face recognition is based upon the grayscale image produced by combining the three color component images. In this work, we consider grayscale image as well as color space in the recognition process. For key frame extractions from a video sequence, the input video is converted to a number of clusters, each of which acts as a linear subspace. The center of each cluster is considered as the cluster representative. Also in this work, for comparing the key frames, the three popular color spaces RGB, YCbCr, and HSV are used for mathematical representation, and the graph-based discriminant analysis is applied for the recognition process. It is also shown that by introducing the intra-class and inter-class similarity graphs to the color space, the problem is changed to determining the color component combination vector and mapping matrix. We introduce an iterative algorithm to simultaneously determine the optimum above vector and matrix. Finally, the results of the three color spaces and grayscale image are compared with those obtained from other available methods. Our experimental results demonstrate the effectiveness of the proposed approach.
