Analyzing Facial Images using Empirical Mode Decomposition for Illumination Artifact Removal and Improved Face Recognition

A popular modality of biometrics, facial recognition is effective when used in controlled environments as in those situations where factors such as camera position, facial expression, and illumination effects are either completely or partially controlled in a beneficial way. Regulation of such factors has an immediate effect on the performance of facial recognition algorithms, in particular illumination effects which can not be controlled by even the most cooperative of users, in this paper we describe a method to address illumination effects in the biometric modality of face recognition using the signal processing analysis tool of empirical mode decomposition (EMD) to decompose images into their intrinsic mode function that correspond to the dominant illumination factors. Using these illumination modes we reconstruct the facial image without these illumination distortion components to synthesize a more illumination neutral facial image. We then perform verification experiments using algorithms such as principal component analysis (PCA), Fisher linear discriminant analysis (FEDA), and advanced correlation filters (ACF´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.