Face Recognition using Multi Region Prominent LBP Representation

Abstract

<p>Various face recognition methods are derived using local features among them the Local Binary Pattern (LBP) approach is very famous. The histogram techniques based on LBP is a complex task. Later Uniform Local Binary Pattern (ULBP) is derived on LBP, based on the bitwise transitions and ULBP’s are treated as the fundamental property of texture. The ULBP approach treated all Non-Uniform Local Binary Patterns’ (NULBP) into one miscellaneous label. Recently we have derived Prominent LBP (PLBP), Maximum PLBP (MPLBP) and Smallest PLBP (SPLBP). The PLBP consists of the majority of the ULBP’s and some of the NULBP’s. The basic disadvantage of these various variants of LBP’s is they are basically local approaches and completely failed in representing features derived from large regions or macrostructures, which are very much essential for faces. This paper derives PLBP’s on the large region. The rectangular region of this paper is assumed with a size of multiples of three and PLBPs are evaluated on dividing each region into multiple regions. The proposed Multi Region-PLBP (MR-PLBP) approach is tested on three facial databases namely Yale, Indian and AT&T ORL. The experimental results show the proposed approach significantly outperforms the other LBP based face recognition methods.</p>

Application of image recognition based on neural network algorithm in multimedia dance teaching

“…) (1) The following describes how to propagate the parameters of multi-layer convolutional neural networks and how to train the parameters. If the gradient of the three vectors is calculated in this way, and the decline speed in the negative gradient direction is the fastest, the update changes of the three vectors are as follows:…”

“…The main content of neural network algorithm includes multi-region image recognition and the design of convolutional neural network algorithm based on MapReduce. Multi-region image recognition is designed based on the traditional convolutional neural network [1]. The structure of its network is simplified, and multiple test areas are divided according to regions during image recognition testing.…”