scholarly journals Pain Expression Recognition Based on pLSA Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Shaoping Zhu
Keyword(s):  
Recognition Accuracy ◽  
Video Sequences ◽  
Expression Recognition ◽  
Class Label ◽  
New Approach ◽  
Visual Words ◽  
Slight Pain ◽  
Pain Expression ◽  
Label Information ◽  
Velocity Information

We present a new approach to automatically recognize the pain expression from video sequences, which categorize pain as 4 levels: “no pain,” “slight pain,” “moderate pain,” and “ severe pain.” First of all, facial velocity information, which is used to characterize pain, is determined using optical flow technique. Then visual words based on facial velocity are used to represent pain expression using bag of words. Final pLSA model is used for pain expression recognition, in order to improve the recognition accuracy, the class label information was used for the learning of the pLSA model. Experiments were performed on a pain expression dataset built by ourselves to test and evaluate the proposed method, the experiment results show that the average recognition accuracy is over 92%, which validates its effectiveness.

scholarly journals Spontaneous Pain Expression Recognition in Video Sequences

2008 ◽  
Author(s):  
Zakia Hammal ◽  
Miriam Kunz ◽  
Martin Arguin ◽  
Frédéric Gosselin
Keyword(s):  
Video Sequences ◽  
Spontaneous Pain ◽  
Expression Recognition ◽  
Pain Expression

scholarly journals Improving the Accuracy of Automatic Facial Expression Recognition in Speaking Subjects with Deep Learning

2020 ◽  
Vol 10 (11) ◽  
pp. 4002
Author(s):  
Sathya Bursic ◽  
Giuseppe Boccignone ◽  
Alfio Ferrara ◽  
Alessandro D’Amelio ◽  
Raffaella Lanzarotti
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Classification Accuracy ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
Video Sequences ◽  
Facial Features ◽  
Expression Recognition ◽  
Lip Reading ◽  
Spatio Temporal

When automatic facial expression recognition is applied to video sequences of speaking subjects, the recognition accuracy has been noted to be lower than with video sequences of still subjects. This effect known as the speaking effect arises during spontaneous conversations, and along with the affective expressions the speech articulation process influences facial configurations. In this work we question whether, aside from facial features, other cues relating to the articulation process would increase emotion recognition accuracy when added in input to a deep neural network model. We develop two neural networks that classify facial expressions in speaking subjects from the RAVDESS dataset, a spatio-temporal CNN and a GRU cell RNN. They are first trained on facial features only, and afterwards both on facial features and articulation related cues extracted from a model trained for lip reading, while varying the number of consecutive frames provided in input as well. We show that using DNNs the addition of features related to articulation increases classification accuracy up to 12%, the increase being greater with more consecutive frames provided in input to the model.

Discriminative Feature Selection in Image Classification and Retrieval

2012 ◽  
pp. 216-230
Author(s):  
Shang Liu ◽  
Xiao Bai
Keyword(s):  
Image Classification ◽  
Bag Of Words ◽  
Current Image ◽  
Visual Words ◽  
Discriminative Feature ◽  
Label Information ◽  
Novel Method ◽  
Category Knowledge ◽  
Supervised Image Classification ◽  
High Level

In this chapter, the authors present a new method to improve the performance of current bag-of-words based image classification process. After feature extraction, they introduce a pairwise image matching scheme to select the discriminative features. Only the label information from the training-sets is used to update the feature weights via an iterative matching processing. The selected features correspond to the foreground content of the images, and thus highlight the high level category knowledge of images. Visual words are constructed on these selected features. This novel method could be used as a refinement step for current image classification and retrieval process. The authors prove the efficiency of their method in three tasks: supervised image classification, semi-supervised image classification, and image retrieval.

Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP

2013 ◽  
pp. 42-55
Author(s):  
Yi Ji ◽  
Khalid Idrissi
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Video Sequences ◽  
Polynomial Kernel ◽  
Expression Recognition ◽  
Emotional States ◽  
Small Set

This paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automatically located from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems.

scholarly journals Optical Flow Filtering-Based Micro-Expression Recognition Method

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2056
Author(s):  
Junjie Wu ◽  
Jianfeng Xu ◽  
Deyu Lin ◽  
Min Tu
Keyword(s):  
Optical Flow ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Recognition Method ◽  
Video Clips ◽  
Low Degree ◽  
Micro Expression

The recognition accuracy of micro-expressions in the field of facial expressions is still understudied, as current research methods mainly focus on feature extraction and classification. Based on optical flow and decision thinking theory, we propose a novel micro-expression recognition method, which can filter low-quality micro-expression video clips. Determined by preset thresholds, we develop two optical flow filtering mechanisms: one based on two-branch decisions (OFF2BD) and the other based on three-way decisions (OFF3WD). In OFF2BD, which use the classical binary logic to classify images, and divide the images into positive or negative domain for further filtering. Differ from the OFF2BD, OFF3WD added boundary domain to delay to judge the motion quality of the images. In this way, the video clips with low degree of morphological change can be eliminated, so as to directly improve the quality of micro-expression features and recognition rate. From the experimental results, we verify the recognition accuracy of 61.57%, and 65.41% for CASMEII, and SMIC datasets, respectively. Through the comparative analysis, it shows that the scheme can effectively improve the recognition performance.

Vector space model for patent documents with hierarchical class labels

2012 ◽  
Vol 38 (3) ◽  
pp. 222-233 ◽  
Author(s):  
Yen-Liang Chen ◽  
Yu-Ting Chiu
Keyword(s):  
Feature Selection ◽  
Vector Space ◽  
Selection Process ◽  
Vector Space Model ◽  
Class Label ◽  
Discriminative Ability ◽  
New Approach ◽  
Space Model ◽  
Patent Documents ◽  
Class Labels

A vector space model (VSM) composed of selected important features is a common way to represent documents, including patent documents. Patent documents have some special characteristics that make it difficult to apply traditional feature selection methods directly: (a) it is difficult to find common terms for patent documents in different categories; and (b) the class label of a patent document is hierarchical rather than flat. Hence, in this article we propose a new approach that includes a hierarchical feature selection (HFS) algorithm which can be used to select more representative features with greater discriminative ability to present a set of patent documents with hierarchical class labels. The performance of the proposed method is evaluated through application to two documents sets with 2400 and 9600 patent documents, where we extract candidate terms from their titles and abstracts. The experimental results reveal that a VSM whose features are selected by a proportional selection process gives better coverage, while a VSM whose features are selected with a weighted-summed selection process gives higher accuracy.

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