P. Jonathon Phillips

Computational and performance aspects of PCA-based face-recognition algorithms

Algorithms based on principal component analysis (PCA) form the basis of numerous studies in the psychological and algorithmic face-recognition literature. PCA is a statistical technique and its incorporation into a face-recognition algorithm requires numerous design decisions. We explicitly state the design decisions by introducing a generic modular PCA-algorithm. This allows us to investigate these decisions, including those not documented in the literature. We experimented with different implementations of each module, and evaluated the different implementations using the September 1996 FERET evaluation protocol (the de facto standard for evaluating face-recognition algorithms). We experimented with (i) changing the illumination normalization procedure; (ii) studying effects on algorithm performance of compressing images with JPEG and wavelet compression algorithms; (iii) varying the number of eigenvectors in the representation; and (iv) changing the similarity measure in the classification process. We performed two experiments. In the first experiment, we obtained performance results on the standard September 1996 FERET large-gallery image sets. In the second experiment, we examined the variability in algorithm performance on different sets of facial images. The study was performed on 100 randomly generated image sets (galleries) of the same size. Our two most significant results are (i) changing the similarity measure produced the greatest change in performance, and (ii) that difference in performance of ±10% is needed to distinguish between algorithms.

Overview of the Multiple Biometrics Grand Challenge

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.

Assessment of time dependency in face recognition: an initial study

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 subjects 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.

The FERET verification testing protocol for face recognition algorithms

Two critical performance characterizations of biometric algorithms, including face recognition, are identification and verification. Identification performance of face recognition algorithms on the FERET tests has been previously reported. We report on verification performance obtained from the Sept96 FERET test. The databases used to develop and test algorithms are usually smaller than the databases that will be encountered in applications. We examine the effects of size of the database on performance for both identification and verification.

Face recognition algorithms and the other‐race effect: computational mechanisms for a developmental contact hypothesis

People recognize faces of their own race more accurately than faces of other races. The “contact” hypothesis suggests that this “other-race effect” occurs as a result of the greater experience we have with own- versus other-race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other-race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other-race effect. Consistent with the predictions of a developmentalcontact hypothesis, “experience-based models” demonstrated an other-race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model’s experience with faces of different races. When the model’s representation relied on a feature set optimized to encode the information in the learned faces, experience-based algorithms recognized minority-race faces more accurately than majority-race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own-race faces. This feature space limits the quality of face representations for other-race faces.

An introduction to the good, the bad, & the ugly face recognition challenge problem

The Good, the Bad, & the Ugly Face Challenge Problem was created to encourage the development of algorithms that are robust to recognition across changes that occur in still frontal faces. The Good, the Bad, & the Ugly consists of three partitions. The Good partition contains pairs of images that are considered easy to recognize. On the Good partition, the base verification rate (VR) is 0.98 at a false accept rate (FAR) of 0.001. The Bad partition contains pairs of images of average difficulty to recognize. For the Bad partition, the VR is 0.80 at a FAR of 0.001. The Ugly partition contains pairs of images considered difficult to recognize, with a VR of 0.15 at a FAR of 0.001. The base performance is from fusing the output of three of the top performers in the FRVT 2006. The design of the Good, the Bad, & the Ugly controls for pose variation, subject aging, and subject “recognizability.” Subject recognizability is controlled by having the same number of images of each subject in every partition. This implies that the differences in performance among the partitions are result of how a face is presented in each image.

Dictionary-based face recognition from video

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.

The challenge of face recognition from digital point-and-shoot cameras

Inexpensive “point-and-shoot” camera technology has combined with social network technology to give the general population a motivation to use face recognition technology. Users expect a lot; they want to snap pictures, shoot videos, upload, and have their friends, family and acquaintances more-or-less automatically recognized. Despite the apparent simplicity of the problem, face recognition in this context is hard. Roughly speaking, failure rates in the 4 to 8 out of 10 range are common. In contrast, error rates drop to roughly 1 in 1,000 for well controlled imagery. To spur advancement in face and person recognition this paper introduces the Point-and-Shoot Face Recognition Challenge (PaSC). The challenge includes 9,376 still images of 293 people balanced with respect to distance to the camera, alternative sensors, frontal versus not-frontal views, and varying location. There are also 2,802 videos for 265 people: a subset of the 293. Verification results are presented for public baseline algorithms and a commercial algorithm for three cases: comparing still images to still images, videos to videos, and still images to videos.

Face recognition vendor test 2002 performance metrics

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.

Empirical evaluation methods in computer vision

Automated performance evaluation of range image segmentation algorithms training/test data partitioning for empirical performance evaluation analyzing PCA-based face recognition algorithms -eigenvector selection and distance measures design of a visual system for detecting natural events by the use of an independent visual estimate - a human fall detector task-based evaluation of image filtering within a class of geometry-driven-diffusion algorithms a comparative analysis of cross-correlation matching algorithms using a pyramidal resolution approach performance evaluation of medical image processing algorithms.