Department of Electronics and Communication Engineering, P.S.N. College of Engineering and Technology, Tirunelveli, Tamil Nadu, India.
J Med Syst. 2018 Dec 7;43(1):12. doi: 10.1007/s10916-018-1128-x.
The eminence of image under test is identified with different methods of Face Recognition (FR) which results in failure due to rapid change in pixel intensity. The identification of similar face with inter class similarity is very difficult in imaging. The imaging technology faces difficult in the mounting of intra class variability because of accommodate, intra-class variability because of head pose, illumination conditions, expressions, facial accessories, aging effects and cartoon faces. In the earliest approach, gradient with Zernike momemts were used to regonize the faces, the performance is low to overcome this a new approach is introduced. Many features of FR are affected by the outcome and low occurrence of performance is observed which is applicable only for data sets that are smaller. The introduction of a new approach can overcome the above stated limitations. This paper describes a novel approach for LS enhancement technique using GSNO and CWSNO, and extracts the PCA features with three ways such as mean, median and mode which are then classified with MD classifier using LOOCV of R-SM to recognize the faces. The performance metrics is also computed and compared. Performance metrics of the proposed approach and the current approach are computed and compared. Thus, the suggested method is useful for increasing the visibility of facial recognition, and overcoming a pose, similarity and illumination problem, which provides a more accurate investigation of the required recognition procedures.
测试图像的显著性采用不同的人脸识别 (FR) 方法来识别，由于像素强度的快速变化，导致识别失败。在成像中，由于类内相似性，相似的人脸识别非常困难。由于容纳、头部姿势、光照条件、表情、面部配饰、老化效应和卡通面孔等因素，成像技术在处理类内可变性方面面临困难。在早期的方法中，使用 Zernike 矩的梯度来识别人脸，但是性能较低，为了克服这一问题，引入了一种新的方法。FR 的许多特征都受到结果的影响，并且观察到性能的低发生率，这种方法仅适用于较小的数据集。引入新方法可以克服上述限制。本文提出了一种使用 GSNO 和 CWSNO 的 LS 增强技术的新方法，并使用三种方法（均值、中位数和众数）提取 PCA 特征，然后使用 LOOCV 的 MD 分类器对其进行分类，以识别人脸。还计算并比较了性能指标。计算并比较了所提出的方法和当前方法的性能指标。因此，该方法有助于提高人脸识别的可视性，克服姿态、相似性和光照问题，为所需识别过程提供更准确的研究。
