Face recognition performance with superresolution
Spotlight summary: Automated facial recognition from imagery is a valuable tool, particularly in law enforcement where it allows screening against criminal databases in situations where that may not otherwise be possible. Due to the importance of its applications, facial recognition has been widely studied and extensively developed (and in recent years incorporated into consumer products such as photograph processing/organization software). High recognition performance is regularly achieved in well controlled, high resolution imagery - for example, as may be seen in a family photograph or in a mugshot. A much more challenging application is facial recognition from video surveillance data, where inexpensive low-resolution imaging equipment is often used, and the subject is typically a significant distance from the camera. In surveillance data the facial image can be expected to occupy only a few tens of pixels, significantly increasing the difficulty of facial recognition.
While an individual video frame may exhibit low resolution, multiple images of the same subject are typically collected. This motivates the use of super-resolution techniques - that is, using computational methods to synthesize a single higher-resolution image from multiple lower-resolution images of the subject. A number of authors have investigated the effects of resolution and super-resolution in facial recognition, and the results have indicated both a high degree of dependence between recognition performance and resolution, and significant potential for performance improvement through super-resolution. Despite the promise of super-resolution, the literature had lacked a comprehensive and quantitative presentation of the improvement that may be realized on realistic data. Here Hu and coworkers present such an analysis. They thoroughly describe the collection of representative test data and evaluate the improvements given using previously-published super-resolution and facial recognition algorithms. A range of relevant test scenarios are considered (e.g., varying ranges and numbers of images), with many cases exhibiting significant improvements with super-resolution. Given the thoroughness of this work, the improvements realized and the quantitative nature of the presented results, I expect this manuscript to be an important touchstone for future work on improved facial recognition through super-resolution.
Technical Division: Information Acquisition, Processing, and Display
ToC Category: Image Processing
|OCIS Codes:||(100.0100) Image processing : Image processing|
|(100.2980) Image processing : Image enhancement|
|(100.6640) Image processing : Superresolution|
|(100.4995) Image processing : Pattern recognition, metrics|
You must log in to add comments.