[Show abstract][Hide abstract] ABSTRACT: Automated human identification is a significant issue in real and virtual societies. Iris is a suitable choice for meeting this goal. In this paper, we present an iris recognition system that uses images acquired in both near-infrared and visible lights. These two types of images reveal different textural information of the iris tissue. We demonstrated the necessity to process both VL and NIR images to recognize irides. The proposed system exploits two feature extraction algorithms: one is based on 1D log-Gabor wavelet which gives a detailed representation of the iris region and the other is based on 1D Haar wavelet which represents a coarse model of iris. The Haar wavelet algorithm is proposed in this paper. It makes smaller iris templates than the 1D log-Gabor approach and yet achieves an appropriate recognition rate. We performed the fusion at the match score level and examined the performance of the system in both verification and identification modes. UTIRIS database was used to evaluate the method. The results were compared with other approaches and proved to have better recognition accuracy, while no image enhancement technique is utilized prior to the feature extraction stage. Furthermore, we demonstrated that fusion can compensate the lack of input image information, which can be beneficial in reducing the computation complexity and handling non-cooperative iris images.
Machine Vision and Applications 05/2014; 25(4):881-899. · 1.44 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper, we study transmission of a memoryless Laplacian source over three types of channels: additive white Laplacian noise (AWLN), additive white Gaussian noise (AWGN), and slow flat-fading Rayleigh channels under both bandwidth compression and bandwidth expansion. For this purpose, we analyze two well-known hybrid digital–analog (HDA) joint source–channel coding schemes for bandwidth compression and one for bandwidth expansion. Then we obtain achievable (absolute-error) distortion regions of the HDA schemes for the matched signal-to-noise ratio (SNR) case as well as the mismatched SNR scenario. Using numerical examples, it is shown that these schemes can achieve a distortion very close to the provided lower bound (for the AWLN channel) and to the optimum performance theoretically attainable bound (for AWGN and Rayleigh fading channels) on mean-absolute error distortion under matched SNR conditions. In addition, a non-linear analog coding scheme is analyzed, and its performance is compared to the HDA schemes for bandwidth compression under both matched and mismatched SNR scenarios. The results show that the HDA schemes outperform the non-linear analog coding over the whole CSNR region.
IEEE Transactions on Communications 01/2014; · 1.75 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper presents a novel and uniform framework for face recognition. This framework is based on a combination of Gabor wavelets, direct linear discriminant analysis (DLDA) and support vector machine (SVM). First, feature vectors are extracted from raw face images using Gabor wavelets. These Gabor-based features are robust against local distortions caused by the variance of illumination, expression and pose. Next, the extracted feature vectors are projected to a low-dimensional subspace using DLDA technique. The Gabor-based DLDA feature vectors are then applied to SVM classifier. A new kernel function for SVM called hyperhemispherically normalized polynomial (HNP) is also proposed in this paper and its validity on the improvement of classification accuracy is theoretically proved and experimentally tested for face recognition. The proposed algorithm was evaluated using the FERET database. Experimental results show that the proposed face recognition system outperforms other related approaches in terms of recognition rate.
[Show abstract][Hide abstract] ABSTRACT: In our previous work, “robust transmission of scalable video stream using modified LT codes”, an LT code with unequal packet protection property was proposed. It was seen that applying the proposed code to any importance-sorted input data, could increase the probability of early decoding of the most important parts when enough number of encoded symbols is available at the decoder’s side. In this work, the performance of the proposed method is assessed in general case for a wide range of loss rate, even when there are not enough encoded symbols at the decoder’s side. Also in this work the degree distribution of input nodes is investigated in more detail. It is illustrated that sorting input nodes in encoding graph, as what we have done in our work, has superior advantage in comparison with unequal input node selection method that is used in traditional rateless code with unequal error protection property.
[Show abstract][Hide abstract] ABSTRACT: Compressed sensing is a new theory that samples a signal below the Nyquist rate. While Gaussian and Bernoulli random measurements perform quite well on the average, structured matrices such as Toeplitz are mostly used in practice due to their simplicity. However, the signal compression performance may not be acceptable. In this paper, we propose to optimize the Toeplitz matrices to improve its compression performance to recover sparse signals. We establish the optimization on minimizing the coherence of the measurement matrix by an intelligent optimization method called Particle Swarm Optimization. Our simulation results show that the optimized Toeplitz matrix outperforms the non-optimized one in reconstructing sparse signals in terms of quality and sampling rate.
Communication and Information Theory (IWCIT), 2013 Iran Workshop on; 01/2013
[Show abstract][Hide abstract] ABSTRACT: In this paper an effective three view multiple human tracking method based on color and texture information fusion is proposed. Since human motion is usually non-linear and non-Gaussian, a particle filter framework is used to estimate human position. Human model is jointly represented by weighted color and cellular LBP (cellular local binary pattern) histograms. Weighted color histogram is robust to scale invariant and partial occlusion but has a main limitation when object's color and background's color are similar; so using these two complement features improve tracking results. This method is robust against illumination changes and occlusions. A three-camera network is used to handle occlusion. Tracking process has done separately for each camera, when occlusion is detected in one view. Tracking results of two other views are used to handle occlusion. Experimental results demonstrate that the proposed method improves performance of human tracking.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we introduce pedestrian detection using combination of low level features like CNN, HOG and Haar with high level features. Two kinds of high level features were used in this paper. One is related to the probability of existence of human's face, which obtained from combination of skin color and possible location and area for the human's face. The other is related to the probability of existence of human's anti-body which obtained by curvature checking of vertical edges, situation of them relative to each other and location of them in the detection window. Several different structures were studied and their results were compared on a diagram. Also the average execution times of them were gathered in a table. At first, we show that appending the high level features to every low level feature improves the performance of detection very much and then, with proper arrangement of several features, it is possible to improve the performance of detection further without increasing the execution time. For evaluation of the proposed algorithm, INRIA database and a video sequence were used.
[Show abstract][Hide abstract] ABSTRACT: Multisensory fusion has become an area of intense research activity in the past few years. The goal of this paper is to present a technique for fusing infrared and visible videos. In this technique we propose a fusion method that quickly fuses infrared and visible frames and gives a better performance. This is done by first decomposing the inputs using DWT and extracting two maps (resulted from Choose Max rule) from approximation sub frames and then fusing detail subframes according to these maps. After being compared to some of the popular fusion methods, the experimental results demonstrate that not only does this proposed method have a superior fusion performance, it can also be easily implemented in visual sensor networks in which speed and simplicity are of critical importance.
[Show abstract][Hide abstract] ABSTRACT: In this paper we propose an approach for behavior modeling and detection of certain types of anomalous behavior. This approach consists of three basic parts. First, we propose busy-idle rates, as the behavior features, to define a behavior model for a block of pixels. Second, given a training set of normal data only, we propose spectral clustering for classifying behaviors wherein block of pixels that exhibit similar behavior models are clustered together. Then a behavior model for each cluster is obtained using the histogram of the samples. Once the behavior models are obtained, we use these models to perform anomalous behavior detection in a test video of the same scene. Experimental results on video surveillance sequences show the effectiveness and speed of proposed method.
Telecommunications (IST), 2012 Sixth International Symposium on; 01/2012
[Show abstract][Hide abstract] ABSTRACT: We study transmission of a memoryless Laplacian source over an average-power limited additive white Laplacian noise (AWLN) channel under bandwidth compression in two cases: 1) matched signal-to-noise ratio (SNR), 2) mismatched SNR. A hybrid digital-analog (HDA) joint source-channel coding (JSCC) scheme is proposed and show that this scheme can achieve a distortion very close to the lower bound on mean-absolute error (MAE) distortion under matched SNR conditions.
Telecommunications (IST), 2012 Sixth International Symposium on; 01/2012
[Show abstract][Hide abstract] ABSTRACT: The objective of image fusion is to combine relevant information from multiple images into a single image. The discrete cosine transform (DCT) based methods of image fusion are more efficient and time-saving in real-time systems using DCT based standards of still image or video. Existing DCT based methods are suffering from some undesirable side effects like blurring or blocking artifacts which reduce the quality of the output image. Furthermore, some of these methods are rather complex and this contradicts the concept of the simplicity of DCT based algorithms. In this paper, an efficient approach for fusion of multi-focus images based on variance calculated in DCT domain is presented. Due to simplicity of our proposed method, it can be easily used in real-time applications. The experimental results verify the efficiency improvement of our method both in output quality and complexity reduction in comparison with several recent proposed techniques.
[Show abstract][Hide abstract] ABSTRACT: LT codes are convenient and popular kind of rateless codes that could easily tolerate different patterns of loss in erasure channels. In this paper an LT code with unequal packet protection (UPP) property is proposed. The proposed code could provide unequal packet recovery to any importance-sorted data packets. Simulation results indicate the enhanced performance of the suggested scheme and its ability to increase the probability of early decoding of more important parts of data rather than the rest. Also it is shown that the proposed scheme could provide comparable bit error rates in comparison to the one of the well known previous methods. The code with modified encoding graph has been utilized for transmitting of the scalable data-partitioned video stream. Simulation results also illustrate the performance of the suggested approach in early delivery of the most important parts of a video sequence.