Abstract
There is a need to assess the impact of filters on the performance of face recognition systems. For this, a standard dataset should be available with relevant filters applied. Currently, such datasets are not publicly available. Some datasets which are available with filters applied, are very low in resolution and thus not relevant for use. To mitigate these limitations, we aim to create a dataset that provides a face recognition database with filters applied to them. The proposed dataset provides high-quality images with ten different filters applied to them. These filters vary from beautification filters and AR-based filters to filters that modify facial landmarks. The wide range of filters including occlusion and beautification that has been applied to the selfies allow a more diverse set of faces to be experimented and analyzed with face recognition and other biometric systems. The dataset will contribute further to the set of facial datasets available publicly. This will allow researchers to study the impact of filters on facial features with a common public benchmark.
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Tiwari, S., Sethia, Y., Tanwar, A., Kumar, R., Dwivedi, R. (2023). FRLL-Beautified: A Dataset of Fun Selfie Filters with Facial Attributes. In: Conte, D., Fred, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science, vol 1875. Springer, Cham. https://doi.org/10.1007/978-3-031-39059-3_30
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DOI: https://doi.org/10.1007/978-3-031-39059-3_30
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