Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keyword Spotting in Poorly Printed Documents using Pseudo 2-D Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel rotationally invariant region-based hidden Markov model for efficient 3-D image segmentation
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
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We introduce a novel approach to gesture recognition, based on Pseudo-3D Hidden Markov Models (P3DHMMs). This technique is capable of integrating spatially and temporally derived features in an elegant way, thus enabling the recognition of different dynamic face-expressions. Pseudo-2D Hidden Markov Models have been utilized for two dimensional problems such as face recognition. P3DHMMs can be considered as an extension of the 2D case, where the so-called superstates in P3DHMM encapsulate P2DHMMs. With our new approach image sequences can efficiently and successfully be processed. Because the 'ordinary' training of P3DHMMs is time expensive and can destroy the 3D approach, an improved training is presented in this paper. The feasibility of the usage of P3DHMMs is demonstrated by a number of experiments on a person independent database, which consists of different image sequences of 4face-expressions from 6 persons.