Deep Heterogeneous Face Recognition Networks Based on Cross-Modal Distillation and an Equitable Distance Metric
| Autor: | Christopher Reale, Heesung Kwon, Hyungtae Lee |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
021110 strategic
defence & security studies Artificial neural network business.industry Computer science 0211 other engineering and technologies Initialization 02 engineering and technology Function (mathematics) Facial recognition system Convolutional neural network Convolution Modal Face (geometry) Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | CVPR Workshops |
| DOI: | 10.1109/cvprw.2017.34 |
| Popis: | In this work we present three methods to improve a deep convolutional neural network approach to near-infrared heterogeneous face recognition. We first present a method to distill extra information from a pre-trained visible face network through the output logits of the network. Next, we put forth an altered contrastive loss function that uses the l1 norm instead of the l2 norm as a distance metric. Finally, we propose to improve the initialization network by training it for more iterations. We present the results of experiments of these methods on two widely used near-infrared heterogeneous face recognition datasets and compare them to the state-of-the-art. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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