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The main challenge in recognizing faces in video is effectively exploiting the multiple frames of a face and the accompanying dynamic signature. One prominent method is based on extracting joint appearance and behavioral features. A second method models a person by temporal correlations of features in a video. Our approach introduces the concept of video-dictionaries for face recognition, which generalizes the work in sparse representation and dictionaries for faces in still images. Video-dictionaries are designed to implicitly encode temporal, pose, and illumination information. We demonstrate our method on the Face and Ocular Challenge Series (FOCS) Video Challenge, which consists of unconstrained video sequences. We show that our method is efficient and performs significantly better than many competitive video-based face recognition algorithms.

Modern vision systems are often a heterogeneous collection of image processing, machine learning, and pattern recognition techniques. One problem with these systems is finding their optimal parameter settings, since these systems often have many interacting parameters. This paper proposes the use of a Genetic Algorithm (GA) to automatically search parameter space. The technique is tested on a publicly available face recognition algorithm and dataset. In the work presented, the GA takes the role of a person configuring the algorithm by repeatedly observing performance on a tuning-subset of the final evaluation test data. In this context, the GA is shown to do a better job of configuring the algorithm than was achieved by the authors who originally constructed and released the LRPCA baseline. In addition, the data generated during the search is used to construct statistical models of the fitness landscape which provides insight into the significance from, and relations among, algorithm parameters.

The goal of the Multiple Biometrics Grand Challenge (MBGC) is to improve the performance of face and iris recognition technology from biometric samples acquired under unconstrained conditions. The MBGC is organized into three challenge problems. Each challenge problem relaxes the acquisition constraints in different directions. In the Portal Challenge Problem, the goal is to recognize people from near-infrared (NIR) and high definition (HD) video as they walk through a portal. Iris recognition can be performed from the NIR video and face recognition from the HD video. The availability of NIR and HD modalities allows for the development of fusion algorithms. The Still Face Challenge Problem has two primary goals. The first is to improve recognition performance from frontal and off angle still face images taken under uncontrolled indoor and outdoor lighting. The second is to improve recognition performance on still frontal face images that have been resized and compressed, as is required for electronic passports. In the Video Challenge Problem, the goal is to recognize people from video in unconstrained environments. The video is unconstrained in pose, illumination, and camera angle. All three challenge problems include a large data set, experiment descriptions, ground truth, and scoring code.

We have successfully implemented a Video-based Automated System for Iris Recognition (VASIR), evaluating its successful performance on the MBGC dataset. The proposed method facilitates the ultimate goal of automatically detecting an eye area, extracting eye images, and selecting the best quality iris image from video frames. The selection method's performance is evaluated by comparing it to the selection performed by humans. Masek's algorithm was adapted to segment and normalize the iris region. Encoding the iris pattern and then completing the matching followed this stage. The iris templates from video images were compared to pre-existing still iris images for the purpose of the verification. This experiment has shown that even under varying illumination conditions, low quality, and off-angle video imagery, that iris recognition is feasible. Furthermore, our study showed that in practice an automated best image selection is nearly equivalent to human selection.

Recent work indicates that state-of-the-art face recognition algorithms can surpass humans matching identity in pairs of face images taken under different illumination conditions. It has been demonstrated further that fusing algorithm- and human-derived face similarity estimates cuts error rates substantially over the performance of the best algorithms. Here we employed a pattern-based classification procedure to fuse individual human subjects and algorithms with the goal of determining whether strategy differences among humans are strong enough to suggest particular man-machine combinations. The results showed that error rates for the pairwise man-machine fusions were reduced an average of 47 percent when compared to the performance of the algorithms individually. The performance of the best pairwise combinations of individual humans and algorithms was only slightly less accurate than the combination of individual humans with all seven algorithms. The balance of man and machine contributions to the pairwise fusions varied widely, indicating that a one-size-fits-all weighting of human and machine face recognition estimates is not appropriate.

With the mass deployment of cameras, concern has risen about protecting a person's privacy as he goes about his daily life. Many of the cameras are installed to perform surveillance tasks that do not require the identity of a person. In the context of surveillance applications, we examine the trade-off between privacy and security. The trade-off is accomplished by looking at quantitative measures of privacy and surveillance performance. To provide privacy protection we examine the effect on surveillance performance of a parametric family of privacy function. A privacy function degrades images to make identification more difficult. By varying the parameter, different levels of privacy protection are provided. We introduce the privacy operating characteristic (POC) to quantitatively show the resulting trade-off between privacy and security. From a POC, policy makers can select the appropriate operating point for surveillance systems with regard to privacy.

We present the methodology and recognition performance characteristics used in the Face Recognition Vendor Test 2002. We refine the notion of a biometric imposter, and show that the traditional measures of identification and verification performance, are limiting cases of the open-universe watch list task. The watch list problem generalizes the tradeoff of detection and identification of persons of interest against a false alarm rate. In addition, we use performance scores on disjoint populations to establish a means of computing and displaying distribution-free estimates of the variation of verification vs. false alarm performance. Finally we formalize gallery normalization, which is an extension of previous evaluation methodologies; we define a pair of gallery dependent mappings that can be applied as a post recognition step to vectors of distance or similarity scores. All the methods are biometric non-specific, and applicable to large populations.

The NIST HumanID Evaluation Framework, or HEF, is an effort to design, implement, and deploy standards for the robust and complete documentation of the biometric system evaluation process. The HEF leverages contemporary technologies, specifically XML, for the formal description of biometric tests. The HEF was used to facilitate the administration of the Face Recognition Vendor Test (FRVT) 2002. Unlike FRVT 2000 or the FERET 1996 evaluations, FRVT 2002 used a large number (over 100,000) of both still and video facial imagery, warranting the development of a more sophisticated and regular means of describing data presented to the participants. The HEF is one component in NIST's ongoing effort to address the need in the biometrics community for a common evaluation framework.

As face recognition research matures and products are deployed, the performance of such systems is being scrutinized by many constituencies. Performance factors of strong practical interest include the elapsed time between a subject's enrollment and subsequent acquisition of an unidentified face image, and the number of images of each subject available. In this paper, a long-term image acquisition project currently underway is described and data from the pilot study is examined. Experimental results suggest that (a) recognition performance is substantially poorer when unknown images are acquired on a different day from the enrolled images, (b) degradation in performance does not follow a simple predictable pattern with time between known and unknown image acquisition, and (c) performance figures quoted in the literature based on known and unknown image sets acquired on the same day may have little practical value.

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. The FERET database is divided into two portions. The development portion is provided to researchers for algorithm development and the sequestered portion provides a set of images not seen by the researchers to test algorithms. The set of test is the third in a sequence of FERET tests. This test was administered in September 1996 and March 1997. The Sept96 test provided a detailed assesment of the state of the art, measurement of algorithm performance on large databases, and a comparison among face recognition algorithms.

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. In this paper, we report on the FERET database and the September 1996 FERET test. This test is the third in a series of supervised face-recognition test administered under the FERET program.