Diego A. Socolinsky

Classification Using Class Cover Catch Digraphs

class cover catch digraphs based on proximity between training observations. Performance comparisons are presented on synthetic and real examples versus k-nearest neighbors, Fishers linear discriminant and support vector machines. We demonstrate that the proposed semiparametric classifier has performance approaching that of the optimal parametric classifier in cases for which the optimal is available for comparison.

Face Recognition in Low-Light Environments Using Fusion of Thermal Infrared and Intensified Imagery

This chapter presents a study of face recognition performance as a function of light level using intensified near infrared imagery in conjunction with thermal infrared imagery. Intensification technology is the most prevalent in both civilian and military night vision equipment and provides enough enhancement for human operators to perform standard tasks under extremely low light conditions. We describe a comprehensive data collection effort undertaken to image subjects under carefully controlled illumination and quantify the performance of standard face recognition algorithms on visible, intensified, and thermal imagery as a function of light level. Performance comparisons for automatic face recognition are reported using the standardized implementations from the Colorado State University Face Identification Evaluation System, as well as Equinoxs algorithms. The results contained in this chapter should constitute the initial step for analysis and deployment of face recognition systems designed to work in low-light conditions.

Using infrared sensor technology for face recognition and human identification

Recent research has demonstrated distinct advantages using thermal infrared imaging for improving face recognition performance. While conventional video cameras sense reflected light, thermal infrared cameras primarily measure emitted radiation from objects at just above room temperature (e.g., faces). Visible and thermal infrared image data collections of frontal views of faces have been on-going at NIST for over two years producing the most comprehensive database known to involve thermal infrared imagery of human faces. Rigorous experimentation with this database has revealed consistently superior recognition performance of algorithms when applied to thermal infrared particularly under variable illumination conditions. An end-to-end face recognition system incorporating simultaneous coregistered thermal infrared and visible has been developed and tested both indoors and outdoors with good performance.

Versatile low-power multispectral video fusion hardware

Image fusion of complementary broadband spectral modalities has been extensively studied for providing performance enhancements to various military applications. With the growing availability of COTS and customized video cameras that image in VIS-NIR, SWIR, MWIR and LWIR, there is a corresponding increase in the practical exploitation of different combinations of fusion between any of these respective spectrums. Equinox Corporation has been developing a unique line of products around the concept of a single unified video image fusion device that can centrally interface with a variety of input cameras and output displays, together with a suite of algorithms that support image fusion across the diversity of possible combinations of these imaging modalities. These devices are small in size, lightweight and have power consumption in the vicinity of 1.5 Watts making them easy to integrate into portable systems.

Face recognition with intensified NIR imagery

This paper presents a systematic study of face recognition performance as a function of light level using intensified near infrared imagery. This technology is the most prevalent in both civilian and military night vision equipment, and provides enough intensification for human operators to perform standard tasks under extremely low-light conditions. We describe a comprehensive data collection effort undertaken by the authors to image subjects under carefully controlled illumination and quantify the performance of standard face recognition algorithms on visible and intensified imagery as a function of light level. Performance comparisons for automatic face recognition are reported using the standardized implementations from the CSU Face Identification Evaluation System. The results contained in this paper should constitute the initial step for analysis and deployment of face recognition systems designed to work in low-light level conditions.