Local Generic Representation for Face Recognition with Single Sample per Person (ACCV, 2014)
(1)Abstract: 1. 每個類別單個樣本的人臉識別（face recognition with single sample per person, SSPP）是一個非常有挑戰性的任務，因爲在這種情況下很難通過標準樣本庫（gallery set）裏面的樣本對待測樣本（query sample）的人臉變化量（facial variation）進行預測; 2. 針對這個問題，這篇論文提出了一個基
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相關文章
- 1. SSPP-DAN: DEEP DOMAIN ADAPTATION NETWORK FOR FACE RECOGNITION WITH SINGLE SAMPLE PER PERSON
- 2. Deep Learning Based Single Sample Per Person Face Recognition: A Survey
- 3. Learning Invariant Deep Representation for NIR-VIS Face Recognition
- 4. Towards Universal Representation Learning for Deep Face Recognition（人臉識別）
- 5. Robust Face Recognition via Sparse Representation
- 6. GhostVLAD for set-based face recognition
- 7. [reading notes]openface for face recognition
- 8. 【FSR】Feature Space Regularization for Person Re-Identification with One Sample
- 9. Stepwise Metric Promotion for Unsupervised Video Person Re-identification
- 10. Re-ID: Learning Deep Feature Representation with Domain Guided Dropout for Person Re-identifiation