Age Gap Reducer-GAN for Recognizing Age-Separated Faces
| Autor: | Mayank Vatsa, Naman Kohli, Richa Singh, Daksha Yadav, Afzel Noore |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
021110 strategic defence & security studies Matching (statistics) Computer Science - Machine Learning Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Age progression 0211 other engineering and technologies Computer Science - Computer Vision and Pattern Recognition Pattern recognition 02 engineering and technology Facial recognition system Machine Learning (cs.LG) Image (mathematics) Visualization Face (geometry) 0202 electrical engineering electronic engineering information engineering Key (cryptography) Identity (object-oriented programming) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ICPR |
| Popis: | In this paper, we propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification. The key idea of this approach is to learn the age variations across time by conditioning the input image on the subject's gender and the target age group to which the face needs to be progressed. The loss function accounts for reducing the age gap between the original image and generated face image as well as preserving the identity. Both visual fidelity and quantitative evaluations demonstrate the efficacy of the proposed architecture on different facial age databases for age-separated face recognition. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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