Face recognition: evaluation report for FaceIt® Identification and Surveillance Jingu Heo * , Besma Abidi, Joonki Paik, and Mongi Abidi. Imaging, Robotics, and Intelligent Systems Laboratory Department of Electrical and Computer Engineering The University of Tennessee, Knoxville ABSTRACT The commercial face recognition software FaceIt® Identification and Surveillance was evaluated using the Facial Recognition Technology (FERET) database. The experimental results show the performance of FaceIt® with variations in illumination, expression, age, head size, pose, and the size of the database which all remain difficult problems in face recognition technology. Keywords : FaceIt®, Identification, Surveillance, FERET, illumination, expression, age, pose, size. 1. INTRODUCTION This paper discusses the experimental results from evaluation of the FaceIt® software. FaceIt® is a face recognition software that uses a Local Feature Algorithm (LFA) and is believed to have the highest accuracy of any commercial facial recognition software 1 . We included the results of both the identification, which uses still images, and the surveillance aspects, which use live video input. 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 the system 2 . Therefore, the FERET Database, which includes 14,126 face images from 1,119 individuals with different variations in face images, was used. The FERET Test Procedures are used in evaluation of FaceIt® 1 . We used not only FERET but also our own database (IRIS Lab at UT), which consists of 34 individuals as a gallery and 768 captured faces from 13 individuals as subjects. A cumulative match characteristic curve (CMC) was used to represent the system’s performance; this procedure is a plot of probabilities of correct matches versus the number of best similarity scores. The CMC curve is typically used for identification (one-to-many searches) 1 . In this paper, we consider the 1 st match and the within first 10 matches regardless of the database size. Sometimes, it is more reasonable to consider 1% or 5% matching results which reflect the database size. The remainder of the paper is structured as follows. Section 2 describes the FaceIt® template. Section 3 presents experimental results of FaceIt® Identification considering challenging problems such as expression, illumination, age, pose, and face size. Section 4 shows the experimental results of FaceIt® Surveillance considering lighting conditions, and variations in faces and database sizes. Section 5 concludes by showing directions for future work in face recognition technology. 2. FACEIT® TEMPLATE WINDOWS Figure 1 shows the FaceIt® software templates. We can add facial images to the template window in (a) by just dragging the images from the windows explorer. The detailed steps are: First, the gallery database, which includes face images, should be created (a). Then, subject images, which we want to identify, should be chosen, either a still image (b) or a live image(c). Finally, after aligning the subject image and then clicking the search button, the match result can be seen with a confidence rate for each rank (d). * E-mail: jheo@utk.edu , Telephone: (865)-974-9685, Fax: (865) 974-5459 J. Heo, B. Abidi, J. Paik, and M. A. Abidi, "Face Recognition: Evaluation Report For FaceIt®," Proc. of SPIE 6th International Conference on Quality Control by Artificial Vision, Vol. 5132, pp. 551-558, Gatlinburg, TN, May 2003. Proc. of SPIE Vol. 5132 551 150