List of Articles Face detection

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  1 - Performance Improvement of the Traditional SVM-Based Face Detection Method
  M. Roohi G. Mirjalily M. T. Sadeghi
  In this paper, we propose some ideas to improve the performance of the traditional face detection based on support vector machine (SVM). The traditional SVM-based system for face detection detects faces by exhaustively scanning an image for face-like patterns at any pos More

  In this paper, we propose some ideas to improve the performance of the traditional face detection based on support vector machine (SVM). The traditional SVM-based system for face detection detects faces by exhaustively scanning an image for face-like patterns at any possible scales. It divides the original image into overlapping sub-images by using a fixed-size cutting window and classifies them using the Support Vector Machine to determine the appropriate class (face or non-face). This approach has not an acceptable detection rate. In this paper to improve the performance, we use cutting windows with different sizes. We fuse the decisions obtained by using different windows. An important issue in the Support Vector Machine classifier is to shift the decision threshold adequately towards the better represented class. In this paper, a novel method is proposed for determining the threshold value adaptively. A post processing algorithm is also presented for reducing the false alarm rate. Experimental results using standard database show that the performance of the proposed SVM-based method is much better than the basic SVM classifier. Manuscript profile
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  2 - Face Detection Using Gabor Filters and Neural Networks
  M. Mahlouji R. Mohammadian
  In this paper, a robust method for face detection from different views using a combination of Gabor filters and neural networks is presented. First, a mathematical equation of Gabor filter is expressed. Then, by examining 75 different filter banks, range of effective pa More

  In this paper, a robust method for face detection from different views using a combination of Gabor filters and neural networks is presented. First, a mathematical equation of Gabor filter is expressed. Then, by examining 75 different filter banks, range of effective parameters values in Gabor filter generation is determined, and finally, the best value for them is specified. The neural network used in this paper is a feed-forward back-propagation multilayer perceptron network. The input vector of the neural network is obtained from the convolution the input image and a Gabor filter with angles π / 2 and the frequency π / 2 in the frequency domain. The proposed method has been tested on 550 image samples from Feret database with simple background and Markus Weber database with complex background, and detection accuracy of them is 98.4% and95%, respectively. Also, the face area has been detected using Viola-Jones algorithm, and then comparison between the results obtained from Viola-Jones algorithm and the proposed method is described. Manuscript profile
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  3 - A High Performance Dual Stage Face Detection Algorithm Implementation using FPGA Chip and DSP Processor
  M V Ganeswara Rao P Ravi  Kumar T  Balaji
  10.52547/jist.31803.10.40.241
  A dual stage system architecture for face detection based on skin tone detection and Viola and Jones face detection structure is presented in this paper. The proposed architecture able to track down human faces in the image with high accuracy within time constrain. A no More

  A dual stage system architecture for face detection based on skin tone detection and Viola and Jones face detection structure is presented in this paper. The proposed architecture able to track down human faces in the image with high accuracy within time constrain. A non-linear transformation technique is introduced in the first stage to reduce the false alarms in second stage. Moreover, in the second stage pipe line technique is used to improve overall throughput of the system. The proposed system design is based on Xil¬inx’s Virtex FPGA chip and Texas Instruments DSP processor. The dual port BRAM memory in FPGA chip and EMIF (External Memory Interface) of DSP processor are used as interface between FPGA and DSP processor. The proposed system exploits advantages of both the computational elements (FPGA and DSP) and the system level pipelining to achieve real time perform¬ance. The present system implementation focuses on high accurate and high speed face detec¬tion and this system evaluated using standard BAO image database, which include images with different poses, orientations, occlusions and illumination. The proposed system attained 16.53 FPS frame rate for the input image spatial resolution of 640X480, which is 23.4 times faster detection of faces compared to MATLAB implementation and 12.14 times faster than DSP implementation and 2.1 times faster than FPGA implementation. Manuscript profile