Face Detection for Video Summaries
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Discriminant Analysis of Stochastic Models and Its Application to Face Recognition
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Image Navigation: A Massively Interactive Model for Similarity Retrieval of Images
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Semantic video fingerprinting and retrieval using face information
Image Communication
Learning to recognize familiar faces in the real world
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Fast person-specific image retrieval using a simple and efficient clustering method
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Movie posters from video by example
Computational Aesthetics'09 Proceedings of the Fifth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
A novel video face clustering algorithm based on divide and conquer strategy
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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This paper presents an image and video indexing approach that combines face detection and face recognition methods. Images of a database or frames of a video sequence are scanned for faces by a neural network-based face detector. The extracted faces are then grouped into clusters by a combination of a face recognition method using pseudo two-dimensional hidden Markov models and a k-means clustering algorithm. Each resulting main cluster consists of the face images of one person. In a subsequent step, the detected faces are labeled as one of the different people in the video sequence or the image database and the occurrence of the people can be evaluated. The results of the proposed approach on a TV broadcast news sequence are presented. It is demonstrated that the system is able to discriminate between three different newscasters and an interviewed person.