Phillips P J
Nat. Inst. of Stand. and Technol., Gaithersburg, MD 20899, USA.
IEEE Trans Image Process. 1998;7(8):1150-64. doi: 10.1109/83.704308.
We present a face identification algorithm that automatically processes an unknown image by locating and identifying the face. The heart of the algorithm is the use of pursuit filters. A matching pursuit filter is an adapted wavelet expansion, where the expansion is adapted to both the data and the pattern recognition problem being addressed. For identification, the filters find the features that differentiate among faces, whereas, for detection, the filters encode the similarities among faces. The filters are designed though a simultaneous decomposition of a training set into a two-dimensional (2-D) wavelet expansion. This yields a representation that is explicitly 2-D and encodes information locally. The algorithm uses coarse to fine processing to locate a small set of key facial features, which are restricted to the nose and eye regions of the Face. The result is an algorithm that is robust to variations in facial expression, hair style, and the surrounding environment. Based on the locations of the facial features, the identification module searches the data base for the identity of the unknown face using matching pursuit filters to make the identification. The algorithm was demonstrated on three sets of images. The first set was images from the FERET data base. The second set was infrared and visible images of the same people. This demonstration was done to compare performance on infrared and visible images individually, and on fusing the results from both modalities. The third set was mugshot data from a law enforcement application.
我们提出了一种面部识别算法，该算法通过定位和识别面部来自动处理未知图像。该算法的核心是使用追踪滤波器。匹配追踪滤波器是一种自适应小波展开，其中的展开既适应数据，也适应所解决的模式识别问题。用于识别时，滤波器找出区分不同面部的特征；而用于检测时，滤波器对不同面部之间的相似性进行编码。这些滤波器通过将训练集同时分解为二维（2-D）小波展开来设计。这产生了一种明确的二维表示，并在局部对信息进行编码。该算法采用从粗到精的处理方式来定位一小部分关键面部特征，这些特征局限于面部的鼻子和眼睛区域。结果得到了一种对面部表情、发型和周围环境变化具有鲁棒性的算法。基于面部特征的位置，识别模块使用匹配追踪滤波器在数据库中搜索未知面部的身份以进行识别。该算法在三组图像上进行了演示。第一组是来自FERET数据库的图像。第二组是同一人群的红外和可见光图像。进行此演示是为了分别比较在红外和可见光图像上的性能，以及融合这两种模态的结果后的性能。第三组是来自执法应用的面部照片数据。
