Shape analysis using generalized procrustes analysis on Active Appearance Model for facial expression recognition

Komalasari, Desy; Widyanto, M. Rahmat; Basaruddin, T.; Liliana, Dewi Yanti

doi:10.1109/icecos.2017.8167123

Cited by 5 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used a vector of coordinates from a set of facial landmarks called shape [51]. All of the shapes were aligned with the standard model, and we studied the shape variation using generalized Procrustes analysis (GPA) [54]. We determined a set of nonrigid parameters for 72 facial landmarks (as seen in Figure 1), and testing the algorithm on 128 different subjects using leave-one-out cross-validation, we determined, through trial and error, that 1500 iterations were required to determine these parameters with over 90% accuracy.…”

Section: Methodsmentioning

confidence: 99%

Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System

Gavrilescu

Vizireanu

2019

Sensors

View full text Add to dashboard Cite

We present the first study in the literature that has aimed to determine Depression Anxiety Stress Scale (DASS) levels by analyzing facial expressions using Facial Action Coding System (FACS) by means of a unique noninvasive architecture on three layers designed to offer high accuracy and fast convergence: in the first layer, Active Appearance Models (AAM) and a set of multiclass Support Vector Machines (SVM) are used for Action Unit (AU) classification; in the second layer, a matrix is built containing the AUs’ intensity levels; and in the third layer, an optimal feedforward neural network (FFNN) analyzes the matrix from the second layer in a pattern recognition task, predicting the DASS levels. We obtained 87.2% accuracy for depression, 77.9% for anxiety, and 90.2% for stress. The average prediction time was 64 s, and the architecture could be used in real time, allowing health practitioners to evaluate the evolution of DASS levels over time. The architecture could discriminate with 93% accuracy between healthy subjects and those affected by Major Depressive Disorder (MDD) or Post-traumatic Stress Disorder (PTSD), and 85% for Generalized Anxiety Disorder (GAD). For the first time in the literature, we determined a set of correlations between DASS, induced emotions, and FACS, which led to an increase in accuracy of 5%. When tested on AVEC 2014 and ANUStressDB, the method offered 5% higher accuracy, sensitivity, and specificity compared to other state-of-the-art methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System

Gavrilescu

Vizireanu

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…To perform these non-shape-related transformations on the training data, we applied the Procrustes analysis approach. [30][31][32] After removing alignments, the principal modes of variation were extracted from training data using PCA 28,29 to build 3D morphable face models (3DMMs). The 3DMMs consisted of the face means and the principal components as modes of variation.…”

Section: Building Face Modelsmentioning

confidence: 99%

Evaluating 3D human face reconstruction from a frontal 2D image, focusing on facial regions associated with foetal alcohol syndrome

2023

View full text Add to dashboard Cite

Foetal alcohol syndrome (FAS) is a preventable condition caused by maternal alcohol consumption during pregnancy. The FAS facial phenotype is an important factor for diagnosis, alongside central nervous system impairments and growth abnormalities. Current methods for analysing the FAS facial phenotype rely on 3D facial image data, obtained from costly and complex surface scanning devices. An alternative is to use 2D images, which are easy to acquire with a digital camera or smart phone. However, 2D images lack the geometric accuracy required for accurate facial shape analysis. Our research offers a solution through the reconstruction of 3D human faces from single or multiple 2D images. We have developed a framework for evaluating 3D human face reconstruction from a single-input 2D image using a 3D face model for potential use in FAS assessment. We first built a generative morphable model of the face from a database of registered 3D face scans with diverse skin tones. Then we applied this model to reconstruct 3D face surfaces from single frontal images using a model-driven sampling algorithm. The accuracy of the predicted 3D face shapes was evaluated in terms of surface reconstruction error and the accuracy of FAS-relevant landmark locations and distances. Results show an average root mean square error of 2.62 mm. Our framework has the potential to estimate 3D landmark positions for parts of the face associated with the FAS facial phenotype. Future work aims to improve on the accuracy and adapt the approach for use in clinical settings.

show abstract

Facial Expression Recognition Based on Curvelet Transform and Sparse Representation

Zhou

et al. 2018

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

View full text Add to dashboard Cite

Shape analysis using generalized procrustes analysis on Active Appearance Model for facial expression recognition

Cited by 5 publications

References 16 publications

Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System

Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System

Evaluating 3D human face reconstruction from a frontal 2D image, focusing on facial regions associated with foetal alcohol syndrome

Facial Expression Recognition Based on Curvelet Transform and Sparse Representation

Contact Info

Product

Resources

About