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 wjert, 2018, Vol. 4, Issue 4, 160-167. Original Article ...
alogrithm for one-sample face recognition using HMM Model of fiducial areas. It used 2D Discrete Wavelet Transform to extract features from images and Hidden Markov Model was used for training, recognition and classification. 90% recognition accuracy was recorded when tested on a subset of AT&T face database. Adedeji et.
Applying Hidden Markov Model for Face Recognition using ...
active research sub-area of face recognition. Finding effective algorithms that deal with this problem is the goal of this dissertation. In order to achieve the goal of building a system recognizing face images, we need a model that can capture selective spatial information. In this dissertation, a Hidden Markov Model (HMM) [10]...
 A paper presentation
Hidden Markov model (HMM) methods. Convolution Neural Network SOM learning based CNN methods . 1. EMBEDDED HIDDEN MARKOV MODEL (HMM): For frontal views the significant facial features appear in a natural order from top to bottom (forehead, eyes, nose, and mouth) and from left to right (e.g. left eye, right eye).
Facial Expression Recognition using Efficient LBP and CNN
al.[8] proposed facial expression recognition method using Hidden Markov Model. The relative displacement of the feature points between the current frame and the neutral frame are extracted as the facial features. Classification entropy threshold and model parameters are found out using iterative algorithm during training process....
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