Face recognition using scattering wavelet under Illicit Drug Abuse variations
Autor: | Prateekshit Pandey, Richa Singh, Mayank Vatsa |
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Rok vydání: | 2016 |
Předmět: |
021110 strategic
defence & security studies Computer science business.industry Feature vector 0211 other engineering and technologies Wavelet transform Pattern recognition 02 engineering and technology Machine learning computer.software_genre Facial recognition system Object-class detection Wavelet Face (geometry) 0202 electrical engineering electronic engineering information engineering Three-dimensional face recognition 020201 artificial intelligence & image processing Artificial intelligence Face detection business computer |
Zdroj: | ICB |
Popis: | 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. |
Databáze: | OpenAIRE |
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