Abstract
This work focuses on the design and validation of a CBR system for efficient face recognition under partial occlusion conditions. The proposed CBR system is based on a classical distance-based classification method, modified to increase its robustness to partial occlusion. This is achieved by using a novel dissimilarity function which discards features coming from occluded facial regions. In addition, we explore the integration of an efficient dimensionality reduction method into the proposed framework to reduce computational cost. We present experimental results showing that the proposed CBR system outperforms classical methods of similar computational requirements in the task of face recognition under partial occlusion.
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Notes
- 1.
In the context of face recognition, partial occlusion refers to the situation where some parts of the faces the system must identify are covered by some artefact.
- 2.
In the context of face recognition, face alignment refers to the task of locating a series of facial key-points in an image, such as eyes, nose, mouth corners, etc.
- 3.
See Sect. 4 for details about the evaluation database.
- 4.
This complexity corresponds to the version of the algorithm which computes and stores dissimilarities in a vector of dimension n. If distances are re-computed to find each nearest neighbour, the complexity is \(\mathcal {O}(knd)\).
- 5.
To ease this, we always select gird sizes such that occlusion units defined as cells in the smallest grid contain an integer number of cells from the bigger grids.
- 6.
Second session images are not available for all individuals in the dataset.
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López-Sánchez, D., Corchado, J.M., González Arrieta, A. (2017). A CBR System for Efficient Face Recognition Under Partial Occlusion. In: Aha, D., Lieber, J. (eds) Case-Based Reasoning Research and Development. ICCBR 2017. Lecture Notes in Computer Science(), vol 10339. Springer, Cham. https://doi.org/10.1007/978-3-319-61030-6_12
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