scholarly journals Landmark-based homologous multi-point warping approach to 3D facial recognition using multiple datasets

2020 ◽  
Vol 6 ◽  
pp. e249 ◽  
Author(s):  
Olalekan Agbolade ◽  
Azree Nazri ◽  
Razali Yaakob ◽  
Abdul Azim Abd Ghani ◽  
Yoke Kqueen Cheah
Keyword(s):  
Feature Analysis ◽  
Reference Object ◽  
Shape Variation ◽  
Bending Energy ◽  
Facial Analysis ◽  
Target Form ◽  
Multiple Datasets ◽  
Facial Landmark ◽  
Template Mesh ◽  
Deformation Approach

Over the years, neuroscientists and psychophysicists have been asking whether data acquisition for facial analysis should be performed holistically or with local feature analysis. This has led to various advanced methods of face recognition being proposed, and especially techniques using facial landmarks. The current facial landmark methods in 3D involve a mathematically complex and time-consuming workflow involving semi-landmark sliding tasks. This paper proposes a homologous multi-point warping for 3D facial landmarking, which is verified experimentally on each of the target objects in a given dataset using 500 landmarks (16 anatomical fixed points and 484 sliding semi-landmarks). This is achieved by building a template mesh as a reference object and applying this template to each of the targets in three datasets using an artificial deformation approach. The semi-landmarks are subjected to sliding along tangents to the curves or surfaces until the bending energy between a template and a target form is minimal. The results indicate that our method can be used to investigate shape variation for multiple datasets when implemented on three databases (Stirling, FRGC and Bosphorus).

scholarly journals An improved boundary-aware face alignment using stacked dense U-Nets

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094090
Author(s):  
Jianghao Ye ◽  
Ying Cui ◽  
Xiang Pan ◽  
Herong Zheng ◽  
Dongyan Guo ◽  
...  
Keyword(s):  
Message Passing ◽  
Face Alignment ◽  
Shape Variation ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Public Datasets ◽  
Pose Variation ◽  
Two Stages ◽  
Landmark Localization ◽  
Boundary Lines

Facial landmark localization is still a challenge task in the unconstrained environment with influences of significant variation conditions such as facial pose, shape, expression, illumination, and occlusions. In this work, we present an improved boundary-aware face alignment method by using stacked dense U-Nets. The proposed method consists of two stages: a boundary heatmap estimation stage to learn the facial boundary lines and a facial landmark localization stage to predict the final face alignment result. With the constraint of boundary lines, facial landmarks are unified as a whole facial shape. Hence, the unseen landmarks in a shape with occlusions can be better estimated by message passing with other landmarks. By introducing the stacked dense U-Nets for feature extraction, the capacity of the model is improved. Experiments and comparisons on public datasets show that the proposed method obtains better performance than the baselines, especially for facial images with large pose variation, shape variation, and occlusions.

scholarly journals 3-Dimensional facial expression recognition in human using multi-points warping

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Olalekan Agbolade ◽  
Azree Nazri ◽  
Razali Yaakob ◽  
Abdul Azim Ghani ◽  
Yoke Kqueen Cheah
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
The State ◽  
Superior Performance ◽  
Reference Object ◽  
Expression Recognition ◽  
Localization Error ◽  
Template Mesh

Abstract Background Expression in H-sapiens plays a remarkable role when it comes to social communication. The identification of this expression by human beings is relatively easy and accurate. However, achieving the same result in 3D by machine remains a challenge in computer vision. This is due to the current challenges facing facial data acquisition in 3D; such as lack of homology and complex mathematical analysis for facial point digitization. This study proposes facial expression recognition in human with the application of Multi-points Warping for 3D facial landmark by building a template mesh as a reference object. This template mesh is thereby applied to each of the target mesh on Stirling/ESRC and Bosphorus datasets. The semi-landmarks are allowed to slide along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal and localization error is assessed using Procrustes ANOVA. By using Principal Component Analysis (PCA) for feature selection, classification is done using Linear Discriminant Analysis (LDA). Result The localization error is validated on the two datasets with superior performance over the state-of-the-art methods and variation in the expression is visualized using Principal Components (PCs). The deformations show various expression regions in the faces. The results indicate that Sad expression has the lowest recognition accuracy on both datasets. The classifier achieved a recognition accuracy of 99.58 and 99.32% on Stirling/ESRC and Bosphorus, respectively. Conclusion The results demonstrate that the method is robust and in agreement with the state-of-the-art results.

Phonomotor Versus Semantic Feature Analysis Treatment for Anomia in 58 Persons With Aphasia: A Randomized Controlled Trial

2019 ◽  
Vol 62 (12) ◽  
pp. 4464-4482 ◽  
Author(s):  
Diane L. Kendall ◽  
Megan Oelke Moldestad ◽  
Wesley Allen ◽  
Janaki Torrence ◽  
Stephen E. Nadeau
Keyword(s):  
Randomized Controlled Trial ◽  
Response Latency ◽  
Controlled Trial ◽  
Semantic Knowledge ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Group Differences ◽  
Randomized Controlled ◽  
Confrontation Naming ◽  
Semantic Feature Analysis

Purpose The ultimate goal of anomia treatment should be to achieve gains in exemplars trained in the therapy session, as well as generalization to untrained exemplars and contexts. The purpose of this study was to test the efficacy of phonomotor treatment, a treatment focusing on enhancement of phonological sequence knowledge, against semantic feature analysis (SFA), a lexical-semantic therapy that focuses on enhancement of semantic knowledge and is well known and commonly used to treat anomia in aphasia. Method In a between-groups randomized controlled trial, 58 persons with aphasia characterized by anomia and phonological dysfunction were randomized to receive 56–60 hr of intensively delivered treatment over 6 weeks with testing pretreatment, posttreatment, and 3 months posttreatment termination. Results There was no significant between-groups difference on the primary outcome measure (untrained nouns phonologically and semantically unrelated to each treatment) at 3 months posttreatment. Significant within-group immediately posttreatment acquisition effects for confrontation naming and response latency were observed for both groups. Treatment-specific generalization effects for confrontation naming were observed for both groups immediately and 3 months posttreatment; a significant decrease in response latency was observed at both time points for the SFA group only. Finally, significant within-group differences on the Comprehensive Aphasia Test–Disability Questionnaire ( Swinburn, Porter, & Howard, 2004 ) were observed both immediately and 3 months posttreatment for the SFA group, and significant within-group differences on the Functional Outcome Questionnaire ( Glueckauf et al., 2003 ) were found for both treatment groups 3 months posttreatment. Discussion Our results are consistent with those of prior studies that have shown that SFA treatment and phonomotor treatment generalize to untrained words that share features (semantic or phonological sequence, respectively) with the training set. However, they show that there is no significant generalization to untrained words that do not share semantic features or phonological sequence features.

Feature Analysis of Groundwater Discharge Points in Coastal Regions around Mt. Chokaisan by Using ALOS AVNIR-2 Data

2011 ◽  
Vol 131 (10) ◽  
pp. 1794-1802
Author(s):  
Yoichi Kageyama ◽  
Chieko Shibata ◽  
Makoto Nishida
Keyword(s):  
Groundwater Discharge ◽  
Feature Analysis ◽  
Coastal Regions

Feature analysis of functional MRI for discrimination between normal and epileptogenic brain

2007 ◽  
Author(s):  
Lauren S. Burrell ◽  
Simon M. Glynn ◽  
George J. Vachtsevanos ◽  
Brian Litt
Keyword(s):  
Functional Mri ◽  
Feature Analysis

Writ Large on Your Face: Observing Emotions Using Automatic Facial Analysis

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Anja Dieckmann ◽  
Matthias Unfried
Keyword(s):  
Facial Analysis

Facial Landmark Detection for Expression Analysis

2019 ◽  
Vol 7 (5) ◽  
pp. 1617-1622
Author(s):  
Takrim Ul Islam Laskar ◽  
Parismita Sarma
Keyword(s):  
Expression Analysis ◽  
Landmark Detection ◽  
Facial Landmark Detection ◽  
Facial Landmark

Feature Analysis of Sketch Based Image Retrieval System

2018 ◽  
Vol 8 (5) ◽  
Author(s):  
Kathy Khaing ◽  
Sai Maung Maung Zaw ◽  
Nyein Aye
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Feature Analysis ◽  
Image Retrieval System

Olfactomedin-like 2A, 2B and 3 are differentially expressed in breast cancer metastases.

2019 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Breast Cancer ◽  
Genetically Engineered ◽  
Differentially Expressed ◽  
Genetically Engineered Mouse Models ◽  
Breast Cancer Metastases ◽  
Multiple Datasets ◽  
Cancer Metastases ◽  
Distant Organ ◽  
Metastatic Process ◽  
Differential Gene

Differential gene expression analysis of multiple datasets, in mice and in men revealed that transcripts of the olfactomedin-like family are differentially expressed in metastases, both in patients with breast cancer and in genetically engineered mouse models of breast cancer. The expression of olfactomedin-like genes was perturbed in metastases to the bone, brain and the lung, suggesting that these molecules function in the metastatic process rather than having tissue-specific associations with the site of dissemination. The olfactomedin-like family may play a role in the progression of breast cancer from frank tumor to colonization of distant organ sites.

Correlative Feature Analysis for Multimodality Breast CAD

2009 ◽  
Author(s):  
Yading Yuan
Keyword(s):  
Feature Analysis
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