The face recognition under the uncontrolled conditions is a widely debated research topic since from last decade due to technology advancement and the emergence of face recognition applications. The uncontrolled conditions such as illumination and pose variations, light intensity variations, etc. lead the poor face recognition performances. The extraction of invariant features in the presence of illumination variations is a difficult task. The variations of light intensity in face images effective in case of large-scale features that are truncated in recent techniques to release clarification mandatory specialties. But the loss of salient features during the release method of small-scale specialties leads to poor face identification performance. In this paper, the robust face descriptor method designed to discuss the challenges of face identification following uncontrolled environments. The proposed framework of variations aware geometric moments and histogram features normalization (VGHN) designed to handle the variations in illumination, pose, and light intensity of face images. In pre-processing, the difference of Gaussian filtering method applied to smooth these variations of each face image. To bridge the semantic recess within the spatial learning and histogram description, we build the face descriptor using the kirsch compass masks to extract the edge directional patterns from the pre-processed image. From each directional pattern, we extracted the variations aware and meaningful features using Geometric moments. The histogram features then removed from the pre-prepared face image. The rich set of features representation of face image has performed by the fusion of geometric moments and histogram features. The artificial neural network and support vector machine used at the end for face recognition and classification purpose. The representation of the VGHN system estimated working various research face datasets. The outcomes show that VGHN was able to improve the robustness compared to existing methods.

自上个十年以来，由于技术的进步和人脸识别应用程序的出现，在不受控制的条件下进行人脸识别已成为引起广泛争议的研究主题。诸如照明和姿势变化，光强度变化等不受控制的条件导致较差的面部识别性能。在存在光照变化的情况下提取不变特征是一项艰巨的任务。面部图像中光强度的变化在大规模特征的情况下有效，这些特征在最近的技术中已被截断以发布明确的强制性专业。但是，在小规模专业的发布方法中，显着特征的丧失导致人脸识别性能不佳。在本文中，鲁棒的人脸描述符方法，旨在讨论不受控制的环境下人脸识别的挑战。提出的变化感知几何矩和直方图框架建议采用归一化（VGHN）功能，以处理面部图像的照度，姿势和光强度变化。在预处理中，采用高斯滤波方法的差异来平滑每个面部图像的这些变化。为了在空间学习和直方图描述之间架起语义凹线，我们使用柯尔希罗盘掩模构建人脸描述符，以从预处理图像中提取边缘方向图样。从每个方向性模式中，我们使用“几何矩”提取了感知变化和有意义的特征。然后，将直方图特征从预先准备的面部图像中删除。通过融合几何矩和直方图特征，可以实现丰富的人脸图像特征表示。最后使用人工神经网络和支持向量机进行人脸识别和分类。VGHN系统的表示估计工作在各种研究面孔数据集上。结果表明，与现有方法相比，VGHN能够提高鲁棒性。