Using Face Quality Ratings to Improve Real-Time Face Recognition
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Expression-invariant face recognition with constrained optical flow warping
IEEE Transactions on Multimedia
Robust Human Face Detection for Moving Pictures Based on Cascade-Typed Hybrid Classifier
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Adaptive frame selection for improved face recognition in low-resolution videos
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Incremental adaptation of fuzzy ARTMAP neural networks for video-based face classification
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Evolution and evaluation of biometric systems
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Human face processing with 1.5D models
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
An evaluation of video-to-video face verification
IEEE Transactions on Information Forensics and Security
An adaptive classification system for video-based face recognition
Information Sciences: an International Journal
Comparison of ARTMAP neural networks for classification for face recognition from video
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Dynamic multi-objective evolution of classifier ensembles for video face recognition
Applied Soft Computing
Face recognition in videos: a graph based modified kernel discriminant analysis
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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This paper presents a number of new views and techniques claimed to be very important for the problem of face recognition in video (FRiV). First, a clear differentiation is made between photographic facial data and video-acquired facial data as being two different modalities: one providing hard biometrics, the other providing softer biometrics. Second, faces which have the resolution of at least 12 pixels between the eyes are shown to be recognizable by computers just as they are by humans. As a way to deal with low resolution and quality of each individual video frame, the paper offers to use the neuro-associative principle employed by human brain, according to which both memorization and recognition of data are done based on a flow of frames rather than on one frame: synaptic plasticity provides a way to memorize from a sequence, while the collective decision making over time is very suitable for recognition of a sequence. As a benchmark for FRiV approaches, the paper introduces the IIT-NRC video-based database of faces which consists of pairs of low-resolution video clips of unconstrained facial motions. The recognition rate of over 95%, which we achieve on this database, as well as the results obtained on real-time annotation of people on TV allow us to believe that the proposed framework brings us closer to the ultimate benchmark for the FRiV approaches, which is "if you are able to recognize a person, so should the computer".