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One-Sample Face Recognition Using HMM Model of Fiducial Areas
One-Sample Face Recognition Using HMM Model of Fiducial Areas . ... This paper describes an effective algorithm forrecognition and verification with one sample image per class. It uses two dimensional discretewavelet transform (2D DWT) to extract features from images; and hidden Markov model (HMM)was used for training, recognition and ...
One-sample Face Recognition Using HMM Model of Fiducial Areas
One-sample Face Recognition Using HMM Model of Fiducial Areas . ... This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images; and hidden Markov model (HMM) was used for training, recognition and ......
Recognizing Frontal Face Images Using Hidden Markov Models ...
In this paper, a low complexity 2-D Hidden Markov Model (HMM) Face Recognition (FR) system is introduced to provide a 2-D representation of the statistical features of the facial image, as opposed ...
 LDA BASED FACE RECOGNITION BY USING HIDDEN MARKOV MODEL IN ...
Abstract: Hidden Markov model (HMM) is a promising method that works well for images with variations in lighting, facial expression, and orientation. Face recognition draws attention as a complex task due to noticeable changes produced on appearance by illumination, facial expression, size, orientation and other external factors....
Seven State HMM-Based Face Recognition System along with ...
J.A. Ojo and S.A. Adeniran, “One-sample Face Recognition Using HMM Model of Fiducial Areas”, International Journal of Image Processing (IJIP), volume 5: Issue-1, pp: 58-68, 2011. S. Raut and S.H.Patil, “Face Recognition using Maximum Confidence Hidden Markov Model”, Proceedings of International Journal of Advances in Engineering and ......
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.
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....
Automatic local Gabor features extraction for face recognition
Automatic local Gabor features extraction for face recognition Yousra BEN JEMAA ... Face recognition is a very challenging area in computer vision ... include hidden Markov model (HMM) [19], elastic bunch graph matching algorithm [3] and local feature analysis.
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]...
Face Recognition using Neural Network - MAFIADOC.COM
1.3.4 Hidden Markov Model Stochastic modeling of nonstationary vector time series based on Hidden Markov Models (HMM) has been very successful for speech applications. Samaria and Fallside [21] applied this method to human face recognition.
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