Abstract
An automatic system for facial expression analysis consists generally of three main phases: detection, feature extraction and classification. In this study, we focus on the extraction of face characteristics (feature extraction) as well as the optimization of the obtained results. The objective is to reduce the number of facial features by removing noisy and redundant data in order to ensure an acceptable facial recognition accuracy while guaranteeing an optimal selection of distinctive facial information. For this purpose, we suggest a new approach based on the combination of principal component analysis (PCA) and ant colony algorithm (ACO). The study was conducted by exploiting the database of the Olivetti Research Laboratory (ORL).
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Khoudda, C., Smaili, E.M., Azzouzi, S., Charaf, M.E.H. (2023). An Enhanced Approach Based on PCA and ACO Methods for Facial Features Optimization. In: Bekkay, H., Mellit, A., Gagliano, A., Rabhi, A., Amine Koulali, M. (eds) Proceedings of the 3rd International Conference on Electronic Engineering and Renewable Energy Systems. ICEERE 2022. Lecture Notes in Electrical Engineering, vol 954. Springer, Singapore. https://doi.org/10.1007/978-981-19-6223-3_2
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DOI: https://doi.org/10.1007/978-981-19-6223-3_2
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