Comparison of body shape descriptors for biometric recognition using MMW images

Abstract

The use of Millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. In this paper, several body shape-based techniques were applied to model the silhouette of images of people acquired at 94 GHz. We put forward several methods for the parameterization and classification stage with the objective of finding the best configuration in terms of biometric recognition performance. Contour coordinates, shape contexts, Fourier descriptors and silhouette landmarks were used as feature approaches and for classification we utilized Euclidean distance and a dynamic programming method. Results showed that the dynamic programming algorithm improved the performance of the system with respect to the baseline Euclidean distance and the necessity of a minimum resolution of the contour to achieve promising equal error rates. The use of the contour coordinates is the most suitable feature to use in the system regarding the performance and the computational cost involved when having at least 3 images for model training. Besides, Fourier descriptors are more robust against rotations, which may be of interest when dealing with few training images.