Wyniki wyszukiwania - BazTech

1

New Fast Principal Component Analysis For Real-Time Face Detection

El-Bakry H. M.

Machine Graphics and Vision

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2009

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Vol. 18, No. 4

405-425

EN

Principal component analysis (PCA) has various important applications, especially in pattern detection, such as face detection and recognition. In real-time applications, the response time must be as short as possible. In this paper, a new implementation of PCA for fast face detection is presented. Such implementation relies on performing cross-correlation in the frequency domain between the input image and eigenvectors (weights). Furthermore, this approach is developed to reduce the number of computation steps required by fast PCA. The "divide and conquer" principle is applied through image decomposition. Each image is divided into smaller-size sub-images, and then each of them is tested separately using a single fast PCA processor. In contrast to using only fast PCA, the speed-up ratio increases with the size of the input image when using fast PCA and image decomposition. Simulation results demonstrate that the proposed algorithm is faster than conventional PCA. Moreover, experimental results for different images show its good performance. The proposed fast PCA increases the speed of face detection, and at the same time does not affect the performance or detection rate.

2

Speeding-up normalized neural networks for face/object detection

El-Bakry H. M., Zhao Q.

Machine Graphics and Vision

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2005

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Vol. 14, No. 1

29-59

EN

Finding an object or a face in an input image is a search problem in the spatial domain. Neural networks have shown good results in detecting a certain face/object in a given image. In this paper, faster neural networks for face/object detection are presented. Such networks are designed based on cross correlation in the frequency domain between the input image and the input weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the search process. The principles of divide and conquer strategy is applied through image decomposition. Each images is divided into small-size sub- images, and then each of them is tested separately using a single faster neural network. Furthermore, the fasted face/object detection is achieved using parallel processing techniques to test the resulting sub-images simultaneously using the same number of faster neural networks. In contrast to using faster neural networks only, the speed-up ratio is increased with the size of the input image when using faster neural networks and image decomposition. Moreover, the problem of local subimage normalization in the frequency domains is solved. The effect of image normalization on the speed-up ratio for face/object detections discussed. Simulation results show that local subimage normalization through weight normalization is faster than subimage normalization in the spatial domain. The overall speed- up ratio of the detection process is increased as the normalization of weights is carried out off line.

3

Human iris detection using fast cooperative modular neural nets and image decomposition

El-Bakry H. M.

Machine Graphics and Vision

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2002

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Vol. 11, No. 4

499-512

EN

In this paper, a combination of fast and cooperative modular neural nets to enhance the performance of the detection process is introduced. I have applied such approach successfully to detect human faces in cluttered scenes, [11]. Here, this technique is used to identify human irises automatically in a given image. Neural nets are used to test whether a window of 20x20 pixels contains an iris or not. The major difficulty in the learning process comes from the large database required for iris/non-iris images . A simple design for cooperative modular neural nets is presemted to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm show a good performance. Moreover, a powerful system for personal identification using iris detection is presented. Futhermore, faster iris detection is obtained through image decomposition into many sub-images and applying cross correlation in the frequency domain between each sub-image and the weights of the hidden layer.

4

Automatic human face recognition using modular neural networks

El-Bakry H. M.

Machine Graphics and Vision

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2001

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Vol. 10, No. 1

47-73

EN

In this paper, a fast biometric system for personal identification through face recognition is introduced. In the detection phase, a fast algorithm for face detection is combined with cooperative modular neural networks (MNNs) to enhance the performance of the detection process. A simple design for cooperative modular neural networks is described to solve this problem by dividing the data into three groups. Furthermore, a new faster face detection approach is presented through image decomposition into many sub-images and applying cross correlation in frequency domain between each sub-image and the weights of the hidden layer. For the recognition phase, a new concept for rotation invariant based on Fourier descriptors and neural networks is presented. Although, the magnitude of the Fourier descriptors is translation invariant, there is no need for scaling or translation invariance. This is because the face sub-image (20 x 20 pixels) is segmented from the whole image during the detection process. The feature extraction algorithm based on Fourier descriptors is modified to reduce the number of neurons is the hidden layer. The second stage extracts wavelet coefficients of the resulted Fourier descriptors before application to neural network. The final vector is fed to a neural net for face classification. Moreover, a modified hierarchy soft decision tree of neural networks is introduced for face recignition. Compared with previous results, the proposed algorithm shown good performance on recognizing human faces with glass, bread, rotation, scaling, occlusion, noise, or change in illumination. Also, the response time is reduced.