Physiological trait-based biometrical authentication of human-face using LGXP and ANN techniques

R. Raja, T. S. Sinha, Rajkumar Patra, S. Tiwari
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引用次数: 35

Abstract

In the recent times, it has been found from the literature that, only front-view of human-face images are used for the authentication of the human being. Very little amount of work has been carried out using side-view and temporal-view of the human-face for the authentication of the human being. The main fact lies in the mentality of present youth, who are very busy in taking the photographs with different poses. Generally the poses are taken from side-view. Hence in the present paper, the main focus has been kept, in the authentication process using methods of recent trends in the field of engineering. The main objective is to handle the variability in human-face appearances due to changes in the viewing direction. Poses, illumination conditions, and expressions are considered as three main parameters, which are processed for the overall authentication process. For the overall processing, extensive feature set like texture, contrast, correlation and shape are extracted by employing modified region growing algorithm and texture feature by local Gabor XOR pattern (LGXP) and artificial neural network (ANN) technique. The present work has been analysed using the data of different subjects with varying ages.
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基于LGXP和人工神经网络技术的人脸生理特征生物认证
近年来,从文献中发现,只有正面的人脸图像被用于人的身份验证。利用人脸侧面图和时间图对人进行身份验证的工作很少。主要原因在于现在年轻人的心态,他们忙着拍各种各样的照片。一般来说,这些姿势都是从侧面拍摄的。因此,在本文中,主要的焦点一直保持在认证过程中使用的方法在工程领域的最新趋势。主要目标是处理由于观看方向的变化而导致的人脸外观的可变性。姿态、光照条件和表情被认为是三个主要参数,它们将被处理用于整个认证过程。在整体处理方面,采用改进的区域生长算法提取纹理、对比度、相关性和形状等广泛的特征集,采用局部Gabor异或模式(LGXP)和人工神经网络(ANN)技术提取纹理特征。目前的工作是使用不同年龄的不同受试者的数据进行分析的。
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