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This paper proposes a method of facial expression recognition based on Zernike moments and the minimum classification error (MCE) based hidden Markov model (HMM). In the feature extraction of face, the method of Zernike moments feature extraction based on local feature regions is adopted. First, eyes and mouths are segmented from the facial expression image and Zernike moments feature vectors of eyes...
There are some problems in the video identity recognition, such as low efficiency of data processing and instability of the identification performance. This paper designs specific links and the corresponding processes for the interested character recognition and scene tracking, which are including the construction of adaptive segmentation, video face flexible recognition, feature library model, optimizing...
In this paper, we show the importance of face-voice correlation for audio-visual person recognition. We evaluate the performance of a system which uses the correlation between audio-visual features during speech against audio-only, video-only and audio-visual systems which use audio and visual features independently neglecting the interdependency of a person's spoken utterance and the associated facial...
For a successful real-time vision-based HCI system, inference from natural visual method is crucial. In this paper, we have aimed to provide interaction through gesture and posture recognition for alphabets and numbers. In addition, data fusion is carried out which integrates these systems to extract multiple meanings at the same time. 3D information is exploited for segmentation and detection of...
The banknote recognition system based on hidden Markov models (HMM) is proposed. It is based on the empirical risk minimization (ERM) principle. Image preprocessing includes brightness equalization and tilt correction. In order to satisfy the high speed and reliability of the banknote processing system, the grid segmentation is used for features extraction. Analyze the experimental data and determine...
In this paper, a probabilistic approach for tracking multiple persons through a network of distributed cameras is presented. The approach deals with the main problems associated with the tracking of persons through wide area networks - bridging large observation gaps between camera views and reidentifying persons - by building on robust and view-invariant high-level features as well as a highly error-tolerant...
Facial expression recognition (FER) from video is an essential research area in the field of human computer interfaces (HCI). In this work, we present a new method to recognize several facial expressions from time sequential facial expression images. To produce robust facial expression features, enhanced independent component analysis (EICA) is utilized to extract locally independent component (IC)...
In this paper we propose an appearance-based approach to recognition of facial action units (AUs) and their temporal segments in frontal-view face videos. Non-rigid registration using free-form deformations is used to determine motion in the face region of an input video. The extracted motion fields are then used to derive motion histogram descriptors. Per AU, a combination of ensemble learners and...
The demand for information services considering personal preferences is increasing. In this paper, we propose a system for automatically acquiring personal preferences from TV viewerpsilas behaviors. Our system firstly extracts intervals of interest and estimates the interest degree for each extracted interval based on the temporal patterns in facial changes by Hidden Markov Models (HMMs). Then, the...
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