Face recognition provides a natural visual interface for human computer interaction （HCI） applications. The process of face recognition, however, is inhibited by variations in the appearance of face images caused by changes in lighting, expression, viewpoint, aging and introduction of occlusion. Although various algorithms have been presented for face recognition, face recognition is still a very challenging topic. A novel approach of real time face recognition for HCI is proposed in the paper. In view of the limits of the popular approaches to foreground segmentation, wavelet multi-scale transform based background subtraction is developed to extract foreground objects. The optimal selection of the threshold is automatically determined, which does not require any complex supervised training or manual experimental calibration. A robust real time face recognition algorithm is presented, which combines the projection matrixes without iteration and kernel Fisher discriminant analysis （KFDA） to overcome some difficulties existing in the real face recognition. Superior performance of the proposed algorithm is demonstrated by comparing with other algorithms through experiments. The proposed algorithm can also be applied to the video image sequences of natural HCI.
人脸姿态变化复杂且对人脸识别性能影响明显,提出了一种融合LCCDN (LSTM and CNN based Cascade Deep Network)与增量聚类的多姿态人脸识别方法。采用LCCDN模型定位人脸关键点,利用长短时记忆网络(LSTM)的记忆功能寻找人脸各关键点在空间上的全局上下文的依赖关系对人脸关键点初始化,并通过卷积神经网络模型,采用由粗到精的策略;定位人脸关键点;以人脸关键点作为人脸朝向描述子,同时为适应人脸姿态不断地动态更新,采用基于熵诱导度量机制的增量聚类方法,对头部姿态进行动态增量聚类,构建人脸姿态池。在此基础上,通过建立不同姿态的人脸识别分类模型实现多姿态人脸识别,在CAS-PEAL-R1、CFP和Multi-PIE三个数据集上的人脸识别准确率分别达到96.75%,96.50%,97.82%。通过与同类人脸识别方法的客观定量对比,实验结果表明所提方法有效、可行。
