Current State of the Art

By 1993 there were several algorithms claiming to have accurate performance in minimally constrained environments. To better understand the potential of these algorithms, DARPA and the Army Research Laboratory established the FERET program with the goals of both evaluating their performance and encouraging advances in the technology [8].

At the time of this writing, there are three algorithms that have demonstrated the highest level of recognition accuracy on large databases (1196 people or more) under double-blind testing conditions. These are the algorithms from University of Southern California (USC) [9], University of Maryland (UMD) [10], and the MIT Media Lab [11]. All of these are participants in the FERET program. Only two of these algorithms, from USC and MIT, are capable of both minimally constrained detection and recognition; the others require approximate eye locations to operate. A fourth algorithm that was an early contender, developed at Rockefeller University [12], dropped from testing to form a commercial enterprise. The MIT and USC algorithms have also become the basis for commercial systems.

The MIT, Rockefeller, and UMD algorithms all use a version of the eigenface transform followed by discriminative modeling. The UMD algorithm uses a linear discriminant, while the MIT system, seen in Figure 3, employs a quadratic discriminant. The Rockefeller system, seen in Figure 2, uses a sparse version of the eigenface transform, followed by a discriminative neural network. The USC system, seen in Figure 1, in contrast, uses a very different approach. It begins by computing Gabor `jets' from the image, and then does a `flexible template' comparison between image descriptions using a graph-matching algorithm.

The FERET database testing employs faces with variable position, scale, and lighting in a manner consistent with mugshot or driver's license photography. On databases of under 200 people and images taken under similar conditions, all four algorithms produce nearly perfect performance. Interestingly, even simple correlation matching can sometimes achieve similar accuracy for databases of only 200 people [8]. This is strong evidence that any new algorithm should be tested with at databases of at least 200 individuals, and should achieve performance over 95% on mugshot-like images before it can be considered potentially competitive.

In the larger FERET testing (with 1166 or more images), the performance of the four algorithms is similar enough that it is difficult or impossible to make meaningful distinctions between them (especially if adjustments for date of testing, etc., are made). On frontal images taken the same day, typical first-choice recognition performance is 95% accuracy. For images taken with a different camera and lighting, typical performance drops to 80% accuracy. And for images taken one year later, the typical accuracy is approximately 50%. Note that even 50% accuracy is 600 times chance performance.

  

Figure: Face Recognition using Local Feature Analysis

  

Figure: Face Recognition using Eigenfaces