Accurate and robust facial expression recognition system using real-time YouTube-based datasets

Ṣiddiqi, Muḥammad Zubair

doi:10.1007/s10489-017-1121-y

Cited by 19 publications

(13 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The entire sub-experiments were performed on every dataset under the absence of the proposed feature extraction method. For these sub-experiments, we utilized recent well-known feature extraction techniques such as wavelet transform [4], Curvelet transform [40], local binary pattern (LBP) [41], local directional pattern (LDP) [42], and stepwise linear discriminant analysis (SWLDA) [43] respectively. The overall results of the sub-experiments are presented in Tabs.…”

Section: Second Experimentsmentioning

confidence: 99%

A Template Matching Based Feature Extraction for Activity Recognition

Ṣiddiqi¹,

Alshammari²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Human activity recognition (HAR) can play a vital role in the monitoring of human activities, particularly for healthcare conscious individuals. The accuracy of HAR systems is completely reliant on the extraction of prominent features. Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities, thereby reducing recognition performance. In this paper, we propose a robust feature extraction method for HAR systems based on template matching. Essentially, in this method, we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette. In this regard, the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame. This process is replicated for the whole frame, and the pixel is directed to the optimum match. The best count is estimated to be the pixel where the silhouette (provided via the template) presented inside the frame. In this way, the feature vector is generated. After feature vector generation, the hidden Markov model (HMM) has been utilized to label the incoming activity. We utilized different publicly available standard datasets for experiments. The proposed method achieved the best accuracy against existing state-of-the-art systems.

show abstract

Section: Second Experimentsmentioning

confidence: 99%

A Template Matching Based Feature Extraction for Activity Recognition

Ṣiddiqi¹,

Alshammari²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Though the above approach may result in extraction of good and required features; however, there is still the possibility of redundancy among those extracted features. To address this redundancy, we apply the stepwise linear discriminant analysis (SWLDA) [39] to the extracted feature space. SWLDA is able to chose the most informative features as it takes advantage of forward selection model, and it can also remove the irrelevant features using the backward regression model.…”

Section: B Feature Extraction and Selectionmentioning

confidence: 99%

Maximum Entropy Markov Model for Human Activity Recognition Using Depth Camera

Alrashdi

Ṣiddiqi²,

Alhwaiti

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

Activity recognition is an essential factor in the determination of daily routine of a human being. There exist numerous Human Activity Recognition (HAR) systems; however, an HAR with a practical accuracy is still in search and a challenge at large. The classifiers utilized in existing systems offer degrading recognition rates in various key environments such as depth camera environment among others. To address the limitation of degrading recognition rates, in this paper, we propose Maximum Entropy Markov Model (MEMM) that solves the degrading recognition rate problem. In MEMM, we model the states of activity recognition as the states of the model itself i.e. as the states of MEMM, and hence consider the observations of video-sensor as the observations of MEMM. Further, we use a modified version of Viterbi, a machine learning algorithm, to generate the most likely probable state sequence based on these observations. Then, from such a state sequence, we use MEMM to predict the activity state. We evaluate the performance MEMM against depth dataset having eleven different types of activities in a large-scale experimentation process. The results show that MEMM outperforms existing well-known methods by achieving a weighted average recognition rate 96.3% across the naturalistic dataset collected using depth camera.

show abstract

“…With the development of deep learning, some studies emphasize the modeling of dynamic shape information of facial expression motion, and then adopt end-to-end deep learning [41,42,[47][48][49], where a 4D face image network for expression recognition uses a number of generated geometric images. A hybrid method uses a contour model to implement face detection, uses a wavelet transform-based method to extract facial expression features, and uses a robust nonlinear method for feature selection; finally, the HMM is used to perform facial expression recognition [50].…”

Section: Face Images For Facial Expression Recognitionmentioning

confidence: 99%

Sample awareness-based personalized facial expression recognition

Wen

2019

Appl Intell

View full text Add to dashboard Cite

The behavior of the current emotion classification model to recognize all test samples using the same method contradicts the cognition of human beings in the real world, who dynamically change the methods they use based on current test samples. To address this contradiction, this study proposes an individualized emotion recognition method based on context awareness. For a given test sample, a classifier that was deemed the most suitable for the current test sample was first selected from a set of candidate classifiers and then used to realize the individualized emotion recognition. The Bayesian learning method was applied to select the optimal classifier and then evaluate each candidate classifier from the global perspective to guarantee the optimality of each candidate classifier. The results of the study validated the effectiveness of the proposed method.

show abstract

Accurate and robust facial expression recognition system using real-time YouTube-based datasets

Cited by 19 publications

References 68 publications

A Template Matching Based Feature Extraction for Activity Recognition

A Template Matching Based Feature Extraction for Activity Recognition

Maximum Entropy Markov Model for Human Activity Recognition Using Depth Camera

Sample awareness-based personalized facial expression recognition

Contact Info

Product

Resources

About