Toward Instantaneous Facial Expression Recognition Using Privileged Information (original) (raw)
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International Journal of Electrical and Computer Engineering (IJECE), 2023
Facial expression recognition is an important part in the field of affective computing. Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypes emotional expressions such as anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. This paper aims to compare feature extraction methods that are used to detect human facial expression. The study compares the gray level co-occurrence matrix, local binary pattern, and facial landmark (FL) with two types of facial expression datasets, namely Japanese female facial expression (JFFE), and extended Cohn-Kanade (CK+). In addition, we also propose an enhancement of extreme learning machine (ELM) method that can adaptively select best number of hidden neurons adaptive ELM (aELM) to reach its maximum performance. The result from this paper is our proposed method can slightly improve the performance of basic ELM method using some feature extractions mentioned before. Our proposed method can obtain maximum mean accuracy score of 88.07% on CK+ dataset, and 83.12% on JFFE dataset with FL feature extraction.
Recognition of Facial Emotions Structures Using Extreme Learning Machine Algorithm
2016
This paper proposes an approach called Extreme Sparse Learning (ESL), which has the ability to jointly learn a dictionary (set of basis) and a non-linear classification model. The proposed approach combines the discriminative power of Extreme Learning Machine (ELM) with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. Additionally, this work presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases. Keywords— Extreme Sparse Learning, non-linear, spatio-temporal. _________________________________________________________________________________________________________________
A Framework of Human Emotion Recognition Using Extreme Learning Machine
Human emotion recognition has been challenging issue in field of human-computer interaction. In order to form an interaction that is more natural between human and computer, the computer should be able to discern and respond to human emotion. In this paper, an approach for recognizing human emotion is proposed. The proposed approach operates HAAR-classifier to detect mouth, eyes, and eyebrow on face, and, to extract features from them, it uses Gabor wavelet. Before classifying the features, PCA is performed to reduce its dimension. The proposed framework employs SLFNs with ELM as its learning algorithm to classify the features. In this experimental, the proposed framework is tested in two cases, personalize and generalize face case, with ten subjects expressing six basic emotions and neural state. The robustness of ELM is evaluated with comparing it to K-NN and SVM. Preliminary experiment shows that the proposed approach has promising performance in personalize face case.
2007
In this work we develop a fast facial expression recognition system based on cross correlation with low complexity by proposing a method that does not need face detection for facial points tracking. Moreover, our simple feature selection according to the facial characteristics differentiates between the six basic expressions (happiness, surprise, sadness, disgust, fear and anger). In this system, 20 selected facial feature points from the first frame to the last are tracked automatically using a cross-correlation optical flow. The extracted feature vector is then given to following classifiers: Bayes optimal classifier with two approaches in probability density function estimation, K-nearest neighbor and support vector machine with radial basis function kernel. These classifiers are analyzed according to their correct classification rate by the cross validation method. For Cohn-Kanade database the best result is obtained by Bayes optimal classifier with the average correct classification rate (Ave-CCR) of 89.67%.
Recognition of Human Facial Expression using Machine Learning Algorithm
IJCSMC, 2019
In the vision of the computer, Human Action Recognition (HAR) plays a huge role in the research area. The human facial expressions convey a lot of information visually rather than articulately. Hence, to identify these facial expression by computer with high recognition rate was a challenging task. To overcome these problem, this paper presents a new technique for human face recognition using the real time videos. The aim of this paper is to recognize the Human Facial Expression like joy, sadness, surprise, fear, anger and disgust from the input video having homogeneous background. The extracted features are given to the pre-processing stage to remove noise and the required features are obtained from the Pre-loaded video sequence. For this gage of work we are using Viola-Jones Algorithm to recognize face, matching algorithms is used for training data, Extreme Sparse Learning and Kernel Extreme Sparse Learning algorithm to recognize the expression of human face. The main application of this works are information security, authentication, biometric identification, video surveillance, data privacy, Human Action Recognition (HAR), Human Computer Interface (HCI), Health Care etc. It also presents the application of the machine learning algorithm to recognize and classification of facial expressions in real time video. Here the pre-loaded video sequence of the human facial expression is given as the input to the MATLAB software to obtain the ideal output. This paper can be implemented using the High Configuration MATLAB simulation software.