Unsupervised segmentation of action segments in egocentric videos using gaze

Hipiny, Irwandi; Ujir, Hamimah; Minoi, Jacey-Lynn; Juan, Sarah Samson; Khairuddin, Mohammad Adib; Sunar, Mohd Shahrizal

doi:10.1109/icsipa.2017.8120635

Cited by 3 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, in order to add a new aerobic routine, we have to manually segment the video into (a maximum of) 3 parts. An automated segmentation of the input video based on the discovery of natural cuts [31] would be ideal. Secondly, a measure of facial expression intensity [32] could be implemented in order to moderate the workout's intensity.…”

Section: Discussionmentioning

confidence: 99%

Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

John

Hipiny

Ujir

2019

2019 International Conference on Computer and Drone Applications (IConDA)

Self Cite

View full text Add to dashboard Cite

It is recommended for a novice to engage a trained and experienced person (i.e., a coach) before starting an unfamiliar aerobic or weight routine. The coach's task is to provide real-time feedbacks to ensure that the routine is performed in a correct manner. This greatly reduces the risk of injury and maximise physical gains. We present a simple image similarity measure based on intersected image region to assess a subject's performance of an aerobic routine. The method is implemented inside an Augmented Reality (AR) desktop app that employs a single RGB camera to capture still images of the subject as he or she progresses through the routine. The background-subtracted body pose image is compared against the exemplar body pose image (i.e., AR template) at specific intervals. Based on a limited dataset, our pose matching function is reported to have an accuracy of 93.67%.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

John

Hipiny

Ujir

2019

2019 International Conference on Computer and Drone Applications (IConDA)

Self Cite

View full text Add to dashboard Cite

show abstract

“…By concentrating on a region of interest (ROI), help to observe the motion of the facial features. For example, to measure human task performance, ROI of gaze is used [12,13,15]. The segmentation process, i.e.…”

Section: Our Workmentioning

confidence: 99%

The analysis of facial feature deformation using optical flow algorithm

Jesemi

Ujir

Hipiny

et al. 2019

IJEECS

View full text Add to dashboard Cite

Facial features deformed according to the intended facial expression. Specific facial features are associated with specific facial expression, i.e. happy means the deformation of mouth. This paper presents the study of facial feature deformation for each facial expression by using an optical flow algorithm and segmented into three different regions of interest. The deformation of facial features shows the relation between facial the and facial expression. Based on the experiments, the deformations of eye and mouth are significant in all expressions except happy. For happy expression, cheeks and mouths are the significant regions. This work also suggests that different facial features' intensity varies in the way that they contribute to the recognition of the different facial expression intensity. The maximum magnitude across all expressions is shown by the mouth for surprise expression which is 9x10-4. While the minimum magnitude is shown by the mouth for angry expression which is 0.4x10-4.

show abstract

“…By concentrating on a region of interest (ROI) help to recognize an AU. For example, to measure human task performance, ROI of gaze is used [16] [17]. Our ROI in this study is the region of the facial points that involve in face muscle deformation.…”

Section: Au Recognition Processmentioning

confidence: 99%

3D Facial Action Units Recognition for Emotional Expression

2019

IJRTE

View full text Add to dashboard Cite

The muscular activities caused the activation of facial action units (AUs) when a facial expression is shown by a human face. This paper presents the methods to recognize AU using a distance feature between facial points which activates the muscles. The seven AU involved are AU1, AU4, AU6, AU12, AU15, AU17 and AU25 that characterizes a happy and sad expression. The recognition is performed on each AU according to the rules defined based on the distance of each facial point. The facial distances chosen are computed from twelve salient facial points. Then the facial distances are trained using Support Vector Machine (SVM) and Neural Network (NN). Classification result using SVM is presented with several different SVM kernels while result using NN is presented for each training, validation and testing phase. By using any SVM kernels, it is consistent that AUs that are corresponded to sad expression has a high recognition compared to happy expression. The highest average kernel performance across AUs is 93%, scored by quadratic kernel. Best results for NN across AUs is for AU25 (Lips parted) with lowest CE (0.38%) and 0% incorrect classification.

show abstract

Unsupervised segmentation of action segments in egocentric videos using gaze

Cited by 3 publications

References 39 publications

Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

The analysis of facial feature deformation using optical flow algorithm

3D Facial Action Units Recognition for Emotional Expression

Contact Info

Product

Resources

About