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Face recognition using scattering wavelet under Illicit Drug Abuse variations
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 1 - 6
Prolonged usage of illicit drugs alter texture and geometric variations of a face and hence, affect the performance of face recognition algorithms. This research proposes a two fold contribution for advancing the state-of-art in recognizing face images with variations caused due to substance abuse: firstly, scattering transform (ScatNet) based face recognition algorithm is proposed. The algorithm yields good results however, it is very expensive in terms of the computational time and space. Therefore, as the next contribution, an autoencoder-style mapping function (AutoScat) is proposed that learns to encode the ScatNet representation of a face image to reduce the computation time. The results are evaluated on the publicly available Illicit Drug Abuse Face database. The results show that ScatNet based face recognition algorithm outperforms two commercial matchers. Further, compared with ScatNet, AutoScat is able to achieve lower rank-1 accuracy but requires 10-3 times lesser computational requirements and around 400 times smaller feature space. © 2016 IEEE.
About the journal
JournalData powered by Typeset2016 International Conference on Biometrics, ICB 2016
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo