E-viri
Recenzirano
Odprti dostop
-
Rashedi, Elaheh; Barati, Elaheh; Nokleby, Matthew; Chen, Xue-wen
Neurocomputing (Amsterdam), 02/2019, Letnik: 329Journal Article
Face recognition tasks have seen a significantly improved performance due to ConvNets. However, less attention has been given to face verification from videos. This paper makes two contributions along these lines. First, we propose a method, called stream loss, for learning ConvNets using unlabeled videos in the wild. Second, we present an approach for generating a face verification dataset from videos in which the labeled streams can be created automatically without human annotation intervention. Using this approach, we have assembled a widely scalable dataset, FaceSequence, which includes 1.5M streams capturing ∼ 500K individuals. Using this dataset, we trained our network to minimize the stream loss. The network achieves accuracy comparable to the state-of-the-art on the LFW and YTF datasets with much smaller model complexity. We also fine-tuned the network using the IJB-A dataset. The validation results show competitive accuracy compared with the best previous video face verification results.
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Vnos na polico
Trajna povezava
- URL:
Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
Ime baze podatkov | Področje | Leto |
---|
Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
---|
Vir: Osebne bibliografije
in: SICRIS
To gradivo vam je dostopno v celotnem besedilu. Če kljub temu želite naročiti gradivo, kliknite gumb Nadaljuj.