Automatic face detection has been intensively studied for human-related recognition systems. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. In this paper, a new face detection algorithm is proposed. This speedy and robust solution developed, on the one hand is based on the segmentation of the color image to skin regions using a new approach to detect the pixels of the skin and the water shed segmentation method. On the other hand, using Gabor filters, combined with a proposed model of face, skin regions are classified into two classes: face and non-face. The integration of these tools in our algorithm permits to develop a face detector with very reasonable and efficient performances. Experimental results show that the method mentioned in this paper can achieve high detection rates and low false positives. To evaluate the detection speed of proposed algorithm, a comparison with a recent known algorithm is made too.
In this paper we propose a new algorithm based on third order cumulants, for MultiCarrier Code Division Multiple Access (MC-CDMA) system equalization. In order to test its efficiency, we have compared with the (CUM-AZ) algorithm proposed in the literature, for that we considered five practical frequency-selective fading channels, called Broadband Radio Access Network (BRAN A, BRAN B, BRAN C, BRAN D, and BRAN E), normalized for (MC-CDMA) system, excited by non-Gaussian sequences. In the part of (MC-CDMA), we use the zero forcing (ZF) and the minimum mean square error (MMSE) equalizers techniques after the channel identification to correct the channel's distortion. The simulation results, in noisy environment and for different signal to noise ratio (SNR), are presented to illustrate the performance of the proposed algorithm.
Head pose estimation has fascinated the research community due to its application in facial motion capture, human-computer interaction and video conferencing. It is a pre-requisite to gaze tracking, face recognition, and facial expression analysis. In this paper, we present a generic and robust method for model-based global 2D head pose estimation from single RGB Image. In our approach we use of the one part the Gabor filters to conceive a robust pose descriptor to illumination and facial expression variations, and that target the pose information. Moreover, we ensure the classification of these descriptors using a SVM classifier. The approach has proved effective view the rate for the correct pose estimations that we got.
The aim of this research is to develop a non-linear blind estimator able to represents a Broadband Radio Access Networks (BRAN) channels. In the one hand, we have used Higher Order Statistics (HOS) theory to build our algorithm. Indeed, we develop a non-linear method based only on fourth order cumulants for identifying the diagonal parameters of quadratic systems. In the other hand, the developed approach is applied to estimate the experimental channels, BRAN A, C and E data normalized for MC-CDMA, in non-linear case. However, the estimated data will be used in the blind equalization. The simulation results in noisy environment and for different signal to noise ratio (SNR) show the accuracy of develop estimator blindly (i.e., without any information about the input) with non-Gaussian signal input. Furthermore, in part of blind equalization problem the obtained results, using Zero forcing (ZF) and Minimum Mean Square Error (MMSE) equalizers, demonstrate that the proposed algorithm is very adequate to correct channel distortion in term the Bit Error Rate (BER). Finally, these estimated data present a necessary asset for conducting validation experiments, and can be also used as a baseline.
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