Image Processing is one of the vibrant research areas nowadays and particularly face recognition is given much importance in all the sectors. Accordingly this research paper proposes a hybrid Face Recognition System to find facial changes due to the aging factor in a robust manner. The highly qualified sharp features are extracted using the algorithms SURF(Speed Up Robust Features), HOG(Histogram of Oriented Gradient) and MSER(Maximally Stable Extremal Regions) to get better results. The proposed method divides the face into five regions. The whole face area is named Region1 can have a complete set of face features extracted using the SURF and it acts as a holistic feature. The Region 2, the nasal bridge features are extracted using the HOG. The Region 3 and Region 4 extract the features of the eyes of the face and the Region 5 extracts the features of the region around the nose and the mouth. The features of these regions are extracted using MSER. These different features from five regions are matched by point matching technique with the database of the target image. Experimental results are evaluated using the datasets such as Yale, FGNET and MORPH dataset. The experimental results show that the proposed face recognition algorithm is superior to traditional methods in terms of recognition rate and time complexity.

图像处理是当今充满活力的研究领域之一，尤其是人脸识别在所有领域中都变得非常重要。因此，本研究论文提出了一种混合型人脸识别系统，用于以健壮的方式发现由于衰老因素而引起的人脸变化。使用SURF（加速鲁棒特征），HOG（定向梯度直方图）和MSER（最大稳定的极值区域）算法提取出高质量的尖锐特征，以获得更好的结果。所提出的方法将面部分为五个区域。整个面部区域名为Region1，可以具有使用SURF提取的完整面部特征集，并且它是整体特征。使用HOG提取区域2的鼻梁特征。区域3和区域4提取脸部眼睛的特征，区域5提取鼻子和嘴巴周围区域的特征。使用MSER提取这些区域的特征。来自五个区域的这些不同特征通过点匹配技术与目标图像的数据库进行匹配。使用Yale，FGNET和MORPH数据集评估实验结果。实验结果表明，提出的人脸识别算法在识别率和时间复杂度上均优于传统方法。使用Yale，FGNET和MORPH数据集评估实验结果。实验结果表明，提出的人脸识别算法在识别率和时间复杂度上均优于传统方法。使用Yale，FGNET和MORPH数据集评估实验结果。实验结果表明，提出的人脸识别算法在识别率和时间复杂度上均优于传统方法。