DiegoVelasquezFaceRecognitionGrandChallenge

DiegoVelasquezFaceRecognitionGrandChallenge - Preliminary...

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Preliminary Face Recognition Grand Challenge Results P. Jonathon Phillips 1 , Patrick J. Flynn 2 , Todd Scruggs 3 Kevin W. Bowyer 2 , William Worek 3 1 National Institute of Standards and Technology, 100 Bureau Dr., Gaithersburg, MD 20899 2 Computer Science & Engineering Depart., U. of Notre Dame, Notre Dame, IN 46556 3 SAIC, 4001 N. Fairfax Dr., Arlington, VA 22203 Abstract The goal of the Face Recognition Grand Challenge (FRGC) is to improve the performance of face recogni- tion algorithms by an order of magnitude over the best results in Face Recognition Vendor Test (FRVT) 2002. The FRGC is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with a data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled con- ditions. This paper presents preliminary results of the FRGC for all six experiments. The preliminary results indicate that significant progress has been made to- wards achieving the stated goals. 1. Introduction In the past few years, a number of new face recog- nition techniques have been proposed. The new tech- niques include recognition from three-dimensional (3D) scans, recognition from high resolution still images, recognition from multiple still images, multi-modal face recognition, multi-algorithm, and preprocessing al- gorithms to correct for illumination and pose variations. These techniques hold the potential to improve perfor- mance of automatic face recognition significantly over the results in the Face Recognition Vendor Test (FRVT) 2002 [1]. The Face Recognition Grand Challenge (FRGC) is designed to achieve an order of magnitude increase in performance over the best results in FRVT 2002 by encouraging the development of algorithms for all of the above proposed methods. To facilitate the devel- opment of new algorithms, a data corpus consisting of 50,000 recordings divided into training and validation partitions was provided to researchers. The starting point for measuring the increase in performance is the high computational intensity test (HCInt) of the FRVT 2002. The images in the HCInt corpus were taken indoors under controlled lighting. The performance point selected as the reference is a verification rate of 80% (error rate of 20%) at a false ac- cept rate (FAR) of 0.1%. This is the performance level of the top three FRVT 2002 participants. An order of magnitude increase in performance is therefore defined as a verification rate of 98% (2% error rate) at the same fixed FAR of 0.1%. Participants in FRGC submitted a set of raw sim- ilarity scores to the FRGC organizers on 14 January 2005. This paper provides a summary of performance from these submitted scores. A more detailed descrip- tion of the FRGC challenge problem, data, and experi- ments is given in Phillips et al [2].
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DiegoVelasquezFaceRecognitionGrandChallenge - Preliminary...

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