Implementation of Missing Data Imputation Schemes in Face Recognition Algorithm under Partial Occlusion
Face detection and recognition algorithms usually assume an image captured from a controlled environment. However, this is not always the case, especially in crowd control under surveillance or footage from a crime scene, where partial occlusions are unavoidable. Unfortunately, these occlusions have...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/7374550 |
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| Summary: | Face detection and recognition algorithms usually assume an image captured from a controlled environment. However, this is not always the case, especially in crowd control under surveillance or footage from a crime scene, where partial occlusions are unavoidable. Unfortunately, these occlusions have an adverse effect on the performance of these classical recognition algorithms. In this study, the performance of some selected data imputation schemes is evaluated on SVD/PCA frontal face recognition algorithm. The experiment was done on two datasets: Jaffe and MIT-CBCL, with immediate confirmation of the adverse effect of occlusion on the facial algorithm without implementing the imputation scheme. Further experimentation shows that IA is an ideal missing data imputation scheme that works best with the SVD/PCA facial recognition algorithm. |
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| ISSN: | 1687-5699 |