Localizing faces in images is a difficult task, and represents the first step towards the solution of the face recognition problem. Moreover, devising an effective face detection method can provide some suggestions to solve similar object and pattern detection problems. This paper presents a novel approach to the solution of the face localization problem using Recursive neural networks (RNNs). The proposed method assumes a graph-based representation of images that combines structural and symbolic visual features. Such graphs are then processed by RNNs, in order to establish the possible presence and the position of faces inside the image. A novel RNN model that can deal with graphs with labeled edges has been also exploited. Some experiments on snapshots from video sequences are reported, showing very promising results.
Bianchini, M., Maggini, M., Sarti, L., Scarselli, F. (2005). Recursive Neural Networks learn to localize faces. PATTERN RECOGNITION LETTERS, 26(12), 1885-1895 [10.1016/j.patrec.2005.03.010].
Recursive Neural Networks learn to localize faces
BIANCHINI, MONICA;MAGGINI, MARCO;SARTI, LORENZO;SCARSELLI, FRANCO
2005-01-01
Abstract
Localizing faces in images is a difficult task, and represents the first step towards the solution of the face recognition problem. Moreover, devising an effective face detection method can provide some suggestions to solve similar object and pattern detection problems. This paper presents a novel approach to the solution of the face localization problem using Recursive neural networks (RNNs). The proposed method assumes a graph-based representation of images that combines structural and symbolic visual features. Such graphs are then processed by RNNs, in order to establish the possible presence and the position of faces inside the image. A novel RNN model that can deal with graphs with labeled edges has been also exploited. Some experiments on snapshots from video sequences are reported, showing very promising results.File | Dimensione | Formato | |
---|---|---|---|
PRL05.pdf
non disponibili
Tipologia:
Post-print
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
347.39 kB
Formato
Adobe PDF
|
347.39 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/21319
Attenzione
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo