Face Recognition using 3D Facial Shape and Color Map Information: Comparison and Combination

In this paper, we investigate the use of 3D surface geometry for face recognition and compare it to one based on color map information. The 3D surface and color map data are from the CAESAR anthropometric database. We find that the recognition performance is not very different between 3D surface and color map information using a principal component analysis algorithm. We also discuss the different techniques for the combination of the 3D surface and color map information for multi-modal recognition by using different fusion approaches and show that there is significant improvement in results. The effectiveness of various techniques is compared and evaluated on a dataset with 200 subjects in two different positions.Comment: Proceedings of SPIE Vol. 5404 Biometric Technology for Human Identification, Anil K. Jain; Nalini K. Ratha, Editors, pp.351-361, ISBN: 9780819453273 Date: 25 August 200

Comparison of Handprinted Digit Classifiers

this report were trained and tested using feature vectors derived from the digit images of NIST Special Database 3 [13]. This database consists of binary 128 by 128 pixel raster images segmented from Normalized Binary Image Feature E tractor Discriminant Functions Class Finder Re ector Hypothesized Class Accept or Re ect Figure 1: Components of Classification System the sample forms of 2100 writers published on CD as [14]. External results on segmentation and recognition of this database have been reported [15]. The relative difficulties of the NIST OCR databases have been discussed in [16]. For this study samples are drawn randomly from the first 250 writers to yield a training set of 7480 digits with a priori class probabilities all equal to 0:1. Even for digits, depending on the application, certain classes may be more prevalent; in banking tasks, for example, "0" is more common. The test set is similarly constructed from the second 250 writers yielding 23140 samples. The images are size normalized by pixel deletion, stroke width is bounded by binary erosion and dilation, and consistent orientation is effected by row shearing. Com onents of Classi cation Syste