Remote Identification of Faces

Face recognition in unconstrained acquisition conditions is one of the most challenging problems that has been actively researched in recent years. It is well known that many state-of-the-art still image-based face recognition algorithms perform well, when constrained (frontal, well illuminated, high-resolution, sharp, and full) face images are acquired. However, their performance degrades significantly when the test images contain variations that are not present in the training images. In this chapter, we highlight some of the key issues in remote face recognition. We define the remote face recognition as one where faces are several tens of meters (10–250 m) from the cameras. We then describe a remote face database which has been acquired in an unconstrained outdoor maritime environment. The face images in this database suffer from variations due to blur, poor illumination, pose, etc. Recognition performance of a subset of existing still image-based face recognition algorithms is discussed on the remote face data set. It is demonstrated that in addition to applying a good classification algorithm, finding features that are robust to variations mentioned above and developing statistical models which can account for these variations are very important for remote face recognition.

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