TY - GEN
T1 - Notice of Removal
T2 - 9th IAPR International Conference on Biometrics, ICB 2016
AU - Boutellaa, Elhocine
AU - Lopez, Miguel Bordallo
AU - Ait-Aoudia, Samy
AU - Feng, Xiaoyi
AU - Hadid, Abdenour
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/23
Y1 - 2016/8/23
N2 - Automatic kinship verification using facial images is a relatively new and challenging research problem in computer vision. It consists in automatically predicting whether two persons have a biological kin relation by examining their facial attributes. While most of the existing works extract shallow handcrafted features from still face images, we approach this problem from spatio-temporal point of view and explore the use of both shallow texture features and deep features for characterizing faces. Promising results, especially those of deep features, are obtained on the benchmark UvA-NEMO Smile database. Our extensive experiments also show the superiority of using videos over still images, hence pointing out the important role of facial dynamics in kinship verification. Furthermore, the fusion of the two types of features (i.e. shallow spatio-temporal texture features and deep features) shows significant performance improvements compared to state-of-the-art methods.
AB - Automatic kinship verification using facial images is a relatively new and challenging research problem in computer vision. It consists in automatically predicting whether two persons have a biological kin relation by examining their facial attributes. While most of the existing works extract shallow handcrafted features from still face images, we approach this problem from spatio-temporal point of view and explore the use of both shallow texture features and deep features for characterizing faces. Promising results, especially those of deep features, are obtained on the benchmark UvA-NEMO Smile database. Our extensive experiments also show the superiority of using videos over still images, hence pointing out the important role of facial dynamics in kinship verification. Furthermore, the fusion of the two types of features (i.e. shallow spatio-temporal texture features and deep features) shows significant performance improvements compared to state-of-the-art methods.
UR - https://www.scopus.com/pages/publications/84988449048
U2 - 10.1109/ICB.2016.7550072
DO - 10.1109/ICB.2016.7550072
M3 - 会议稿件
AN - SCOPUS:84988449048
T3 - 2016 International Conference on Biometrics, ICB 2016
BT - 2016 International Conference on Biometrics, ICB 2016
A2 - Fierrez, Julian
A2 - Li, Stan Z.
A2 - Ross, Arun
A2 - Veldhuis, Raymond
A2 - Alonso-Fernandez, Fernando
A2 - Bigun, Josef
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 June 2016 through 16 June 2016
ER -