Header menu link for other important links
MDLFace: Memorability augmented deep learning for video face recognition
G. Goswami, R. Bhardwaj, ,
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 1 - 7
Videos have ample amount of information in the form of frames that can be utilized for feature extraction and matching. However, face images in not all of the frames are 'memorable' and useful. Therefore, utilizing all the frames available in a video for recognition does not necessarily improve the performance but significantly increases the computation time. In this research, we present a memorability based frame selection algorithm that enables automatic selection of memorable frames for facial feature extraction and matching. A deep learning algorithm is then proposed that utilizes a stack of denoising autoencoders and deep Boltzmann machines to perform face recognition using the most memorable frames. The proposed algorithm, termed as MDLFace, is evaluated on two publicly available video face databases, Youtube Faces and Point and Shoot Challenge. The results show that the proposed algorithm achieves state-of-the-art performance at low false accept rates. © 2014 IEEE.
About the journal
JournalData powered by TypesetIJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo