On the effects of dimensionality reduction on high dimensional similarity search
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
On the effects of dimensionality on data analysis with neural networks
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Robust detection of outliers for projection-based face recognition methods
Multimedia Tools and Applications
Outliers in some Faces and non-Faces data
International Journal of Biometrics
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In this paper, the impact of outliers on the performance of high-dimensional data analysis methods is studied in the context of face recognition. Most of the existing face recognition methods are based on PCA-like methods: Faces are projected into a lower dimensional space in which similarity between faces is more easily evaluated. These methods are, however, very sensitive to the quality of face images used in the training and the recognition phases. Their performance significantly degrades when faces are not well centered or taken under variable illumination conditions. In this paper, we study this phenomenon for two face recognition methods (PCA and LDA2D) and we propose a filtering process that allows an automatic isolation of noisy faces which are responsible for the performance degradation. This process is performed during the training phase as well as the recognition phase. It is based-on the recently proposed robust high-dimensional data analysis method RobPCA. Experiments show that this filtering process improves the recognition rate by 10 to 20%.