Face Perception Methodologies with Principal Component Methodology Algorithm
Syed Umar, P. Gayathri
Journal of Multimedia Processing and Technologies, Vol. 10, No. 4, 2019.
DOI: https://doi.org/10.6025/jmpt/2019/10/4/133-137
Link: http://www.dline.info/jmpt/fulltext/v10n4/jmptv10n4_1.pdf
Abstract:
In this article, two new facial recognition techniques based on a single vector Decomposition (SVD). Here is
the method of the principal component method (PCM) and linear vector discriminate (LVD) applied to the facial features, the
main component of the detection face recognition problem. The human face is full of information, but it works all the
information that is time-consuming and inefficient. Better, special and important information and other information that is
not useful that the system is an effective solution. The main component of the method aspects of face recognition. Vectors and
face their own value calculated by a series of portraits. New faces projected onto the surface of the expanded clean room and
a weighted sum of their face. The weights are used to detect the face. The Euclidian distance in time and space for growth in
the face of this complexity using two singular value decomposition methods. The simulation results show that the effectiveness
of the proposed faces recognition technology. Here is the face used principal component method and linear discriminate
method series of special value. To Test, the data shows that the face of the proposed algorithm influences the complexity its
accuracy. Basis, it is concluded that the major part of the flex can achieve good results than other methods. The results also
realized that the time complexity of the proposed method significantly reduces promote the effective implementation of the
proposed method.