Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition
| Autor: | Nasser M. Nasrabadi, Christopher Reale, Rama Chellappa, Heesung Kwon |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
021110 strategic
defence & security studies Matching (statistics) Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 02 engineering and technology Overfitting Convolutional neural network Facial recognition system Image (mathematics) Face (geometry) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | CVPR Workshops |
| DOI: | 10.1109/cvprw.2016.47 |
| Popis: | Heterogeneous face recognition is the problem of identifying a person from a face image acquired with a nontraditional sensor by matching it to a visible gallery. Most approaches to this problem involve modeling the relationship between corresponding images from the visible and sensing domains. This is typically done at the patch level and/or with shallow models with the aim to prevent overfitting. In this work, rather than modeling local patches or using a simple model, we propose to use a complex, deep model to learn the relationship between the entirety of cross-modal face images. We describe a deep convolutional neural network based method that leverages a large visible image face dataset to prevent overfitting. We present experimental results on two benchmark datasets showing its effectiveness. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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