Authors:
Torsten Schlett
;
Christian Rathgeb
;
Juan Tapia
and
Christoph Busch
Affiliation:
da/sec - Biometrics and Security Research Group, Hochschule Darmstadt, Germany
Keyword(s):
Biometrics, Face Images, Dataset Cleaning, Mislabeling, Image Hash, Face Recognition, Quality Assessment.
Abstract:
Various face image datasets intended for facial biometrics research were created via web-scraping, i.e. the collection of images publicly available on the internet. This work presents an approach to detect both exactly and nearly identical face image duplicates, using file and image hashes. The approach is extended through the use of face image preprocessing. Additional steps based on face recognition and face image quality assessment models reduce false positives, and facilitate the deduplication of the face images both for intra- and inter-subject duplicate sets. The presented approach is applied to five datasets, namely LFW, TinyFace, Adience, CASIA-WebFace, and C-MS-Celeb (a cleaned MS-Celeb-1M variant). Duplicates are detected within every dataset, with hundreds to hundreds of thousands of duplicates for all except LFW. Face recognition and quality assessment experiments indicate a minor impact on the results through the duplicate removal. The final deduplication data is made av
ailable at https://github.com/dasec/dataset-duplicates.
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