From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A context model for ubiquitous computing applications
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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This paper proposes a preprocessing filter fusion for efficient face recognition.Since no priori knowledge of system working environment can be assumed. The proposed method can decide an optimal configuration of filter by exploring the filter fusion to unknown illumination conditions. In this paper, we propose to investigate how to preprocess an input face image for the task of robust face recognition, especially in changing illumination environment (bad illumination). We found that the performance of each preprocessing method for compensating illumination is highly affected by working illumination environment. Changing illumination poses a most challenging problem in face recognition. A previous research for illumination compensation has been investigated. The illumination filter includes Retinex filter, end-in contrast stretching and histogram equalization filter. The proposed method has been tested to robust face recognition in varying illumination conditions (our lab, FERET DB). We made in illumination cluster using combined FART and RBF, K-means algorithm. Extensive experiment shows that the proposed system can achieve very encouraging performance in varying illumination environments. We furthermore show how this algorithm can be extended towards face recognition across illumination.