Neural network-based face detection

Rowley, Henry A.; Baluja, Shumeet; Kanade, Takeo

doi:10.1109/34.655647

Cited by 2,779 publications

(618 citation statements)

References 16 publications

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“…Nowadays, neural networks have shown very good results for detecting a certain pattern in a given image (Rowley et al 1998;Feraud et al 2000;Anifantis et al 1999;Lang et al 1988;El-Bakry and Zhao 2005). Among other techniques (Schneiderman and Kanade 1998; Zhu et al 2000;Srisuk and Kurutach 2002;Bao et al 2006), neural networks are efficient pattern detectors (Rowley et al 1998;Feraud et al 2000;El-Bakry and Zhao 2005;Essannouni and Ibn Elhaj 2006;Roth et al 2006;Ramasubramanian and Kannan 2006). But the problem with neural networks is that the computational complexity is very high because the networks have to process many small local windows in the images (Zhu et al 2000;Srisuk and Kurutach 2002;Yang et al 2002).…”

Section: Introductionmentioning

confidence: 96%

A novel high-speed neural model for fast pattern recognition

El-Bakry

2009

Soft Comput

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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.

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Section: Introductionmentioning

confidence: 96%

A novel high-speed neural model for fast pattern recognition

El-Bakry

2009

Soft Comput

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“…Pattern recognition in the context of appearance based face detection can be approached in several ways (Yang et al, 2002). Techniques proposed for this task are for example the Neural Network (NN) (Rowley et al, 1998), probabilistic modelling (Schneiderman and Kanade, 1998), cascade of boosted features (AdaBoost) (Viola and Jones, 2001), Sparse Network of Winnows (SNoW) (Gundimada and Asari, 2005), combination of AdaBoost and SNoW (Osuna et al, 1997) and the Support Vector Machine (SVM) (Ruan andYin, 2009). …”

Section: Introductionmentioning

confidence: 99%

Human Face Detection and Recognition using Web-Cam

S.¹

2012

Journal of Computer Science

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Problem statement:The illuminance insensitivity that reflects the angle of human facial aspects occurs once the distance between the object and the camera is too different such as animated images. This has been a problem for facial recognition system for decades. Approach: For this reason, our study represents a novel technique for facial recognition through the implementation of Successes Mean Quantization Transform and Spare Network of Winnow with the assistance of Eigenface computation. After having limited the frame of the input image or images from Web-Cam, the image is cropped into an oval or eclipse shape. Then the image is transformed into greyscale color and is normalized in order to reduce color complexities. We also focus on the special characteristics of human facial aspects such as nostril areas and oral areas. After every essential aspectsarescrutinized, the input image goes through the recognition system for facial identification. In some cases where the input image from the Web-Cam does not exist in the database, the user will be notified for the error handled. However, in cases where the image exists in the database, that image will be computed for similarity measurement using Euclidean Distance measure from the input image. Results and Conclusion:The result of our experiment reveals that the recognition process of 150 images in the database and 10 images from the Web-Cam provides 100% accuracy in terms of recognition. The runtime in this case is at 0.04 sec.

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“…Neural networks are widely used for image processing applications such as face recognition and face detection [22,37,44]. Also, neural networks are the algorithm of choice when it comes to autonomous driving [34,35].…”

Section: Related Workmentioning

confidence: 99%