Experimental Analysis of LDA & HMM

Publisher:
INTERNATIONAL FORUM OF RESEARCHERS STUDENTS AND ACADEMICIAN
Publication Type:
Journal Article
Citation:
IFRSA International Journal of Data Warehousing & Mining (IIJDWM), 2012, 2 (3), pp. 150 - 155 (6)
Issue Date:
2012-08-14
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While traditional face recognition is typically based on still images, face recognition from video sequences has become popular recently. This work describes a new method to perform face recognition from video sequences. Faces are detected and tracked in a video sequence using Hidden Markov Model. This process also allows locating and extracting facial feature regions around the eyes, nose and mouth. During the training process, the statistics of training video sequences of each subject, and the temporal dynamics, are learned by an HMM. During the recognition process, the temporal characteristics of the test video sequence are analyzed over time by the HMM corresponding to each subject. Conventional techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and feature based Elastic Bunch Graph Matching (EBGM) and 2D and 3D face models are well known for face detection and recognition.
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