We propose a novel method for training Convolution Neural Network, named CNN-FQ, which takes a face image and outputs a scalar summary of the image quality. The CNN-FQ is trained from triplets of faces that are automatically labeled based on responses of a pre-trained face matcher. The quality scores extracted by the CNN-FQ are directly linked to the probability that the face matcher incorrectly ranks a randomly selected triplet of faces. We applied the proposed CNN-FQ, trained on CASIA database, for selection of the best quality image from a collection of face images capturing the same identity. The quality of the single face representation was evaluated on 1:1 Verification and 1:N Identification tasks defined by the challenging IJB-B protocol. We show that the recognition performance obtained when using faces selected based on the CNN-FQ scores is significantly higher than what can be achieved by competing state-of-the-art image quality extractors.
CITATION STYLE
Yermakov, A., & Franc, V. (2021). CNN Based Predictor of Face Image Quality. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12666 LNCS, pp. 679–693). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-68780-9_52
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