期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2019
卷号：15
期号：5
页码：1
DOI：10.1177/1550147719845276
出版社：Hindawi Publishing Corporation
摘要：In order to facilitate effective crime prevention and to issue timely warnings for the sake of public security, it is important to pinpoint the accurate position of particular pedestrians in crowded areas. Face recognition is the most popular method to detect and track pedestrian movement. During the face recognition process, feature classification ability and reliability are determined by the feature extraction methods. The primary challenge for researchers is to obtain a stable result while the targeted face is subject to varying conditions—particularly of illumination. To address this issue, we propose a novel pedestrian detection algorithm with multisource face images, which involves a face recognition algorithm based on the conjugate orthonormalized partial least-squares regression analysis under a complex lighting environment. Statistical learning theory is a research specialization of machine learning, especially applicable to small samples. Building upon the theoretical principles used to solve small-sample statistical problems, a new hypothesis has been developed; using this concept, we integrate the conjugate orthonormalized partial least-squares regression with the revised support vector machine algorithm to undertake the solution of the facial recognition problem. The experimental result proves that our algorithm achieves better performance when compared with other state-of-the-art methodologies, both numerically and visually.
关键词：Pedestrian detection; face recognition; data fusion; conjugate orthonormalized partial least squares
