Summary
In this paper we present a model of face class appearance based on learning a relation between features and a face invariant. We have developed a face invariant model for accurate face localization in natural images that presents face pose changes. A probabilistic model capture a relationship between features appearance and invariant geometry is then used to infer a face instance in new image. We use local features which the performances of appearance distinctiveness are sufficient to localize face. An EM classification is applied to determine exactly the face appearance features. Then, invariants parameters are predicted and hierarchical clustering method achieve invariant geometric localization, where clustering deep depends on the aggregate value considered as a factor of precision to construct clusters of invariants. The appearance probabilities of features are computed to select the best cluster and thus to localize face in image. We evaluate our generic invariant by testing it in face detection experiments on PIE, FERET and CMU-Profiles databases. The experimental results show that using face invariant gives a localization rate of 89.3% and results in high accuracy face localization.
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Taffar, M., Benmohammed, M. (2011). Generic Face Invariant Model for Face Detection. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing, vol 102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23154-4_5
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DOI: https://doi.org/10.1007/978-3-642-23154-4_5
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