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Special Issue on Facial Image Processing and Analysis

Facial image processing and analysis methods have numerous applications in visual surveillance, computer and physical access control, and digital entertainment. Since facial images are commonly used on various identification documents, these methods have a great potential to become the next generation biometric technology. As facial images can be acquired even without subjects' cooperation, they constitute often the only biometric information available in some visual data databases. // Though there has been a great deal of progress in face detection and recognition in the last few years, many problems remain unsolved. Rigorous testing and verification on real-world datasets often reveals that today's facial image processing and analysis systems still lack the robustness required for reliable deployment. As an example, face detection must cope with many challenging problems, especially when dealing with outdoor illumination, pose variation with large rotation angles, low image quality, low resolution, occlusion, and background changes characteristic of complex real-life scenes. The design of face recognition algorithms that are effective over a wide range of viewpoints, complex outdoor lighting, occlusions, facial expressions and aging is still a major area of research. There is a continuous need to improve detection and recognition, mode and evaluate performance, and develop real-time biometric systems. // This special issue focuses on recent advances in facial image processing and analysis that address the above problems and explores emerging biometric applications. The purpose is to fill an existing gap in the scientific literature by presenting recent progress and to provide benchmarks and reference points for future developments. // The idea of putting together the Special Issue on Facial Image Processing and Analysis was suggested to the Editors-In-Chief by the Guest Editors in November 2006. In December 2006, the outline and schedule of the special issue were established by the Guest Editors, and the first call for papers was distributed through the Internet. // Since May to December 2007, forty-six manuscripts were submitted for possible inclusion in the special issue. Each one of the submitted manuscripts was reviewed by at least three experts in the field. Between December 2007 and March 2008, the first round of reviews was completed with thirty papers removed from further consideration. Between September and November 2008, revised versions of the remaining papers underwent an additional review by the Guest Editors who eventually selected twelve articles for the special issue. The final issue was finalized in February 2009. // The papers included in the special issue cover great diversity in the methods and applications proposed for facial image processing and analysis. It is our hope that the accepted papers deal with relevant practical issues and challenging problems that will prove valuable to both researchers and practitioners. // The special issue opens with a paper on "Precise Eye and Mouth Localization" by P. Campadelli, R. Lanzarotti, and G. Lipori. It presents a method which is based on support vector machines trained on optimally chosen Haar wavelet coefficients. In another paper on facial feature localization, entitled "Towards Practical Facial Feature Detection", M. Eckhardt, I. Fasel, and J. Movellan develop the idea of context dependent inference. Their real-time system first uses robust detectors to detect contexts in which target features occur and then employs refined detectors trained to localize the features given the detected context. // As the choice of a method for representing common patterns is a critical factor in the design of robust facial image detection and classification systems, M. Ashraful Amin and H. Yan present "An Empirical Study on the Characteristics of Gabor Representations for Face Recognition, " which compares the classification capability of different Gabor representations for human face recognition and introduces a cost-effective variant of Gabor feature extraction methods. In the next paper, entitled "Face Detection and Recognition Using Maximum Likelihood Classifiers on Gabor Graphs", M. Günther and R. P. Würtz propose an integrated face detection and recognition system that combines various classifiers and Gabor graphs. // Feature extraction is the central theme in "Subspaces versus Submanifolds: A Comparative Study in Small Sample Size Problem" by H. Huang, J. Li, and H. Feng. The authors compare a number of unsupervised, supervised, and kernel-based methods for face recognition. In another paper on feature extraction, "Robust Adapted Principal Component Analysis for Face Recognition", S. Chen, B. C. Lovell and T. Shan deal simultaneously with large variations in illumination, expression and pose using only a single gallery image per person. // In "Facial Biometrics Using Non-tensor Product Wavelet and 2D Discriminant Techniques", D. Zhang, X. You, P. Wang, S. N. Yanushkevich, and Y. Y. Tang propose a non-tensor product bivariate wavelet coupled with a modified two-dimensional linear discriminant technique that allows the detection of particular facial features in the high-frequency components. To compensate for pose changes and limited occlusion and distortions, F. Li and H. Wechsler propose "Face Authentication Using Recognition-by-Parts, Boosting and Transduction". This method involves feature selection of local patch instances including dimensionality reduction, exemplar-based clustering of patches into parts, and data fusion for matching using boosting driven by parts that play the role of weak-learners. // The next two papers focus on face analysis for digital video applications. In the paper entitled "Multi-Scale Dynamic Features Based Driver Fatigue Detection", B. Yin, X. Fan, and Y. Sun extend the idea of the local binary patterns based on Gabor features to account for the temporal aspect of human fatigue. In another paper, T. Germa, F. Lerasle, and T. Simon present "Video-Based Face Recognition and Tracking from a Robot Companion". The integrated still-to-video face recognition system proposed uses a number of features as persistent cues in a robust and probabilistically motivated way. // The special issue concludes with two papers on expression analysis and synthesis. I. Buciu and I. Nafornita present "Feature Extraction through Cross-Phase Congruency for Facial Expression Analysis". The authors use phase congruency maps to obtain discriminative features for facial expression analysis. In the paper entitled "Facial Expression Synthesis Based on Facial Component Model", L. Xiong, N. Zheng, S. Du, and J. Liu divide a face region into several partitions and construct local texture models to synthesize facial expressions.

facial image processing; facial analysis; face recognition

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