利用改进的局部二值模式和基于Tani-Moto的最近邻算法实现人脸图像的性别分类
Developing a gender classification approach in human face images using modified local binary patterns and tani-moto based nearest neighbor algorithm
摘要：
人体识别是计算机视觉中备受关注的问题。性别分类作为身份识别的前处理步骤，在身份识别中起着重要的作用。到目前为止，已经提出了各种方法来解决这个问题。当然，分类精度是性别分类研究人员面临的主要挑战。但是，在智能手机图像采集中可能会出现旋转、灰度变化、姿态、光照变化等挑战。在这方面，本文提出了一种多步骤的方法来分类人脸图像的性别基于改进的局部二值模式（MLBP）。LBP是一种纹理描述子，它提取了局部对比度和局部空间结构信息。诸如噪声敏感性、旋转敏感性和低鉴别力特征等问题可以被认为是基本LBP的缺点。MLBP利用一种新的理论对基本LBP的二值模式进行分类，克服了MLBP的缺点。拟议的办法包括两个阶段。首先，基于MLBP算法提取人脸图像的特征向量。接下来，非线性分类器可以用于对性别进行分类。本文采用Tani-Moto度量作为距离测度，对最近邻分类器进行评价。在结果部分，我们使用了两个数据库，self-collected和ICPR作为人脸数据库。结果进行了比较，一些国家的最先进的算法在这方面的文献中，所提出的方法的准确率显示出高品质。所提出的方法的其他一些主要优点是旋转不变性、低噪声敏感性、尺寸不变性和低计算复杂性。所提出的方法降低了智能手机应用程序的计算复杂性，因为减少了数据库比较的数量。由于内存和CPU使用减少，它还可以提高智能手机中同步应用程序的性能。
英文摘要：
Human identification is a much attention problem in computer vision. Gender classification plays an important role in human identification as preprocess step. So far, various methods have been proposed to solve this problem. Absolutely, classification accuracy is the main challenge for researchers in gender classification. But, some challenges such as rotation, gray scale variations, pose, illumination changes may be occurred in smart phone image capturing. In this respect, a multi step approach is proposed in this paper to classify genders in human face images based on improved local binary patters (MLBP). LBP is a texture descriptor, which extract local contrast and local spatial structure information. Some issues such as noise sensitivity, rotation sensitivity and low discriminative features can be considered as disadvantages of the basic LBP. MLBP handle disadvantages using a new theory to categorize extracted binary patterns of basic LBP. The proposed approach includes two stages. First of all, a feature vector is extracted for human face images based on MLBP. Next, non linear classifiers can be used to classify gender. In this paper nearest neighborhood classifier is evaluated based on Tani-Moto metric as distance measure. In the result part, two databases, self-collected and ICPR are used as human face database. Results are compared by some state-ofthe-art algorithms in this literature that shows the high quality of the proposed approach in terms of accuracy rate. Some of other main advantages of the proposed approach are rotation invariant, low noise sensitivity, size invariant and low computational complexity. The proposed approach decreases the computational complexity of smartphone applications because of reducing the number of database comparisons. It can also improve performance of the synchronous applications in the smarphones because of memory and CPU usage reduction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
