An approach for face detection using artificial intelligence

Vandana S. Bhat, J. Pujari
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Abstract

This paper proposes a method for detecting faces in a given image by combining Gabor filter and Neural network. The first phase uses gabor filter which generates a feature set. Face and non face templates is taken and processed with gabor filter.The face images are present in spatial (time) domain. The conversion of images into frequency domain is processed through inverse fast fourier transform. The subsequent frequency domain images is conjugated with gabor filter bank and feature vector is generated. The second phase involves a method where all the features are given as input to neural network of 2 hidden layer with scaled conjugate training. Thus this approach being deployed, is a convolution of Gabor filter with frequency domain of training and test images provided a feature vector that was sourced to neural network. Proposed system was tested and the results indicated the efficient performance.
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一种基于人工智能的人脸检测方法
本文提出了一种将Gabor滤波与神经网络相结合的人脸检测方法。第一阶段使用gabor滤波器生成特征集。人脸模板和非人脸模板采用gabor滤波器进行处理。人脸图像存在于空间(时间)域。通过快速傅里叶反变换将图像转换到频域。随后的频域图像与gabor滤波器组进行共轭,生成特征向量。第二阶段的方法是将所有特征作为输入输入到2隐层神经网络中,并进行缩放共轭训练。因此,这种方法被部署,是一个卷积的Gabor滤波器与频域的训练和测试图像提供了一个特征向量,来源神经网络。对该系统进行了测试,结果表明该系统具有良好的性能。
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