Empirical mode decomposition for removal of specular reflections and cast shadow effects
Ramamurthy Bhagavatula, Marios Savvides · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Facial recognition is fast becoming one of the more popular and effective modalities of biometrics when used in controlled environments. Controlled environments are those in which factors such as facial expression, pose, camera position, and in particular illumination effects are controlled to some degree with respect to better performance. Regulation or normalization of such factors has effects on all facial recognition algorithms, and the factor of illumination effects is one of significant importance. In this paper we describe a method to address illumination effects in the biometric modality of face recognition using Empirical Mode Decomposition (EMD) to identify illumination modes that compose the image. Following identification of intrinsic mode functions that correspond to the dominant illumination factors, we reconstruct the facial image minus these negative factors to synthesize a more neutral facial image. We then perform recognition and verification experiments using different algorithms such as Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA), and Correlation Filters (CF's) to demonstrate the fundamental effectiveness of EMD as an illumination compensation method. Results are reported on the Carnegie Mellon University Pose-Illumination-Expression (CMU PIE) Database and the Yale Face Database B.