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Recent Advances in Face Recognition

Neural networks have shown good results for detecting a certain pattern in a given image. In this paper, faster neural networks for pattern detection are presented. Such processors are designed based on cross correlation in the frequency domain between the input matrix and the input weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the detection process. The principle of divide and conquer strategy is applied through matrix decomposition. Each matrix is divided into smaller in size submatrices and then each one is tested separately by using a single faster neural processor. Furthermore, faster pattern detection is obtained by using parallel processing techniques to test the resulting submatrices at the same time using the same number of faster neural networks. In contrast to faster neural networks, the speed up ratio is increased with the size of the input matrix when using faster neural networks and matrix decomposition. Moreover, the problem of local submatrix normalization in the frequency domain is solved. The effect of matrix normalization on the speed up ratio of pattern detection is discussed. Simulation results show that local submatrix normalization through weight normalization is faster than submatrix normalization in the spatial domain. The overall speed up ratio of the detection process is increased as the normalization of weights is done off line.

New faster normalized neural networks for sub-matrix detection using cross correlation in the frequency domain and matrix decomposition

Performance analysis of Bayesian networks and neural networks in classification of file system activities

An efficient algorithm for pattern detection using combined classifiers and data fusion

Neural networks have shown good results for detecting a certain pattern in a given image. In this paper, faster neural networks for pattern detection are presented. Such processors are designed based on cross-correlation in the frequency domain between the input matrix and the input weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the searching process. The principle of divide and conquer strategy is applied through matrix decomposition. Each matrix is divided into smaller in size sub-matrices and then each one is tested separately using a single faster neural processor. Furthermore, faster pattern detection is obtained using parallel processing techniques to test the resulting submatrices at the same time using the same number of faster neural networks. In contrast to faster neural networks, the speed up ratio is increased with the size of the input matrix when using faster neural networks and matrix decomposition. Moreover, the problem of local sub-matrix normalization in the frequency domain is solved. The effect of matrix normalization on the speed up ratio of pattern detection is discussed. Simulation results show that local sub-matrix normalization through weight normalization is faster than sub-matrix normalization in the spatial domain. The overall speed up ratio of the detection process is increased as the normalization of weights is done off line.

A novel high-speed neural model for fast pattern recognition

In this paper we present a neural detector of frontal faces in gray scale images under arbitrary face size, orientation, facial expression, skin color, lighting conditions and background environment. In a two-level process, a window normalization module reduces the variability of the features and a neural classifier generates multiple face position hypotheses. Extended experiments carried out in a test-bed of 6406 face images, have shown that the face detection accuracy is increased significantly when non-linear and probabilistic illumination equalizers pre-process the sub-images. Moreover, better results can be achieved in case of training the neural detector using positional and orientation normalized face examples. In this case the neural face detector has the capability to locate both position and orientation of a face. In the multiple face position hypotheses generated by the proposed neural method, 98.3% detection accuracy, the highest reported in the literature, was measured.

D. Anifantis

Papers

A neural network method for accurate face detection on arbitrary images