Learn More
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. To date, 14,126(More)
Advances in residual vector quantization (RVQ) are surveyed. Definitions of joint encoder optimality and joint decoder optimality are discussed. Design techniques for RVQs with large numbers of stages and generally different encoder and decoder codebooks are elaborated and extended. Fixed-rate RVQs, and variable-rate RVQs that employ entropy coding are(More)
Two critical performance characterizations of biometric algorithms, including face recognition, are identiication and veriication. In face recognition, FERET is the de facto standard evaluation methodology. Identiication performance of face recognition algorithms on the FERET tests has been previously reported. In this paper we report on veriication(More)
I n this paper]-, a modular clutter rejection technique using region-based principal component analysis (PCA) is proposed. Our modular clutter rejection system uses dynamic ROI extraction to overcome the problem of poorly centered targets. I n dynamic R O I extraction, a representative R O I is moved in several directions with respect to the center of the(More)
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(More)
Two key performance characterization of biometric algorithms (face recognition in particular) are (1) verification performance and (2) and performance as a function of database size and composition. This characterization is required for developing robust face recognition algorithms and for successfully transitioning algorithms from the laboratory to real(More)