Combined and Weighted Features for Robust Multispectral Face Recognition

Abstract

Face recognition has been very popular in recent years, for its advantages such as acceptance by the wide public and the price of cameras, which became more accessible. The majority of the current facial biometric systems use the visible spectrum, which suffers from some limitations, such as sensitivity to light changing, pose and facial expressions. The infrared spectrum is more relevant to facial biometric, for its advantages such as robustness to illumination change. In this paper, we propose two multispectral face recognition approaches that use both the visible and infrared spectra. We tested the new approaches with Uniform Local Binary Pattern (uLBP) as a local descriptor and Zernike Moments as a global descriptor on IRIS Thermal/Visible and CSIST Lab 2 databases. The experimental results clearly demonstrate the effectiveness of our multispectral face recognition system compared to a system that uses a single spectrum.

Thermal Biometric Features for Drunk Person Identification Using Multi-Frame Imagery

“…Face recognition procedures as well as their performance are seriously affected by illumination conditions when visible light is used [6]. Thermal infrared [7,8] can be used to capture information from faces. This procedure is actually a passive approach, i.e., the thermal detector records only the energy radiated from the face according to its temperature, which mainly depends on the physiological condition of the person [9].…”