TY - GEN
T1 - Türk TV dizileri çok-pozlu yüz veritabani
AU - Ozlu, Ahmet
AU - Ekenel, Hazim Kemal
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/5
Y1 - 2018/7/5
N2 - Facial image processing and analysis has numerous applications. Recently, with deep learning based approaches, a significant performance improvement has been obtained. An important factor affecting the performance of the deep learning-based systems is availability of large amount of data. Therefore, recently, a couple of large-scale face datasets that contain still images of subjects have become publicly available. However, a video face database that contains both a large number of subjects as well as a large number of samples per subject has not been available, yet. In this study, to fulfill this need, Turkish TV series have been used, and preparation of a multi-view face sequence dataset has been focused. In the study, state-of-the-art off-the-shelf tools have been utilized for face detection, face alignment, and face recognition and the steps we have followed to collect the database are presented. Our goal is, upon completion of the processes, to reach a face database of at least 3000 subjects, each having at least 100 video sequences. In the database, along with the identity labels, we also plan to generate age and gender labels. We believe that this database would become an invaluable resource for automatic facial image processing and analysis research, especially, for the approaches that aim at exploiting dynamic features.
AB - Facial image processing and analysis has numerous applications. Recently, with deep learning based approaches, a significant performance improvement has been obtained. An important factor affecting the performance of the deep learning-based systems is availability of large amount of data. Therefore, recently, a couple of large-scale face datasets that contain still images of subjects have become publicly available. However, a video face database that contains both a large number of subjects as well as a large number of samples per subject has not been available, yet. In this study, to fulfill this need, Turkish TV series have been used, and preparation of a multi-view face sequence dataset has been focused. In the study, state-of-the-art off-the-shelf tools have been utilized for face detection, face alignment, and face recognition and the steps we have followed to collect the database are presented. Our goal is, upon completion of the processes, to reach a face database of at least 3000 subjects, each having at least 100 video sequences. In the database, along with the identity labels, we also plan to generate age and gender labels. We believe that this database would become an invaluable resource for automatic facial image processing and analysis research, especially, for the approaches that aim at exploiting dynamic features.
KW - Deep learning
KW - Facial image processing and analysis
KW - Multi-view face database
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U2 - 10.1109/SIU.2018.8404767
DO - 10.1109/SIU.2018.8404767
M3 - Conference contribution
AN - SCOPUS:85050805010
T3 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
SP - 1
EP - 4
BT - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Y2 - 2 May 2018 through 5 May 2018
ER -