Unsupervised Feature Learning with Single Layer ICANet for Face Recognition
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Compared to supervised learning, unsupervised learning allows systems to learn consistent patterns from cheap and abundant unlabeled data, without the need for manual annotation. In this paper, we present a novel unsupervised feature learning method with single layer (SL) network based independent component analysis (ICA) filters called SL-ICANet, with the goal of achieving compact and robust facial feature representation. Our contributions are twofold: (i) We developed a single-layer convolutional network for unsupervised learning wherein the trainable kernels are replaced by ICA filters. (ii) We extended our SL-ICANet to use multi-scale information for better feature learning. Extensive experiments on two popular face recognition benchmarks, namely, labeled faces in the wild and facial recognition technology show that the proposed method might serve as a simple but highly competitive baseline for face recognition.
KeywordsUnsupervised learning Face recognition LFW FERET ICANet
This work is supported by National Natural Science Foundation of China (Grant Nos. 61402307, 61702058) National Key Scientific Instrument and Equipment Development Project of China (No. 2013YQ49087903), the Scientific Research Foundation of CUIT (Nos. KYTZ201717, J201706) and the China Postdoctoral Science and Foundation No. 2017M612948.
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