Papers by Olalekan Agbolade
Symmetry, Jul 17, 2020
One of the most pertinent applications of image analysis is face recognition and one of the most ... more One of the most pertinent applications of image analysis is face recognition and one of the most common genetic disorders is Down syndrome (DS), which is caused by chromosome abnormalities in humans. It is currently a challenge in computer vision in the domain of DS face recognition to build an automated system that equals the human ability to recognize face as one of the symmetrical structures in the body. Consequently, the use of machine learning methods has facilitated the recognition of facial dysmorphic features associated with DS. This paper aims to present a concise review of DS face recognition using the currently published literature by following the generic face recognition pipeline (face detection, feature extraction, and classification) and to identify critical knowledge gaps and directions for future research. The technologies underlying facial analysis presented in recent studies have helped expert clinicians in general genetic disorders and DS prediction.
PLOS ONE, Apr 9, 2020
Background: The application of three-dimensional scan models offers a useful resource for studyin... more Background: The application of three-dimensional scan models offers a useful resource for studying craniofacial variation. The complex mathematical analysis for facial point acquisition in three-dimensional models has made many craniofacial assessments laborious. Method: This study investigates three-dimensional (3D) soft-tissue craniofacial variation, with relation to ethnicity, sex and age variables in British and Irish white Europeans. This utilizes a geometric morphometric approach on a subsampled dataset comprising 292 scans, taken from a Liverpool-York Head Model database. Shape variation and analysis of each variable are tested using 20 anchor anatomical landmarks and 480 sliding semi-landmarks. Results: Significant ethnicity, sex, and age differences are observed for measurement covering major aspects of the craniofacial shape. The ethnicity shows subtle significant differences compared to sex and age; even though it presents the lowest classification accuracy. The magnitude of dimorphism in sex is revealed in the facial, nasal and crania measurement. Significant shape differences are also seen at each age group, with some distinct dimorphic features present in the age groups. Conclusions: The patterns of shape variation show that white British individuals have a more rounded head shape, whereas white Irish individuals have a narrower head shape. White British persons also demonstrate higher classification accuracy. Regarding sex patterns, males are relatively larger than females, especially in the mouth and nasal regions. Females presented with higher classification accuracy than males. The differences in the chin, mouth, nose, crania, and forehead emerge from different growth rates between the groups. Classification accuracy is best for children and senior adult age groups.
IEEE Access
Down syndrome (DS) is one of the prominent neuro-developmental diseases which are distinguished b... more Down syndrome (DS) is one of the prominent neuro-developmental diseases which are distinguished by atypical fractionation behaviors, physical traits, and other mental disabilities. Current techniques of recognizing the syndrome need genetic testing through clinical studies, which is usually expensive and challenging to get. In order to simplify the classification approach, computer-aided facial analysis methods incorporating machine learning and morphometrics are crucial. Thus, this study proposes Homologous Anatomical-based Histogram of Oriented Gradients plus Support Vector Machine (HAB-HOG/SVM) to automatically detects and extracts 74 homologous facial landmarks from the subjects (DS patient and healthy control) face image and Chord-Transformed Principal Components (CT-PC) as features extraction method for classification. The novelty of this method relies on the automatic acquisition of landmark data which is conceptually simple, robust, computationally efficient, and annotation errorfree and the feature extraction technique applies which is simplified enough to follow. The experiment reports recognition accuracy of 56.82% and 98.86% for Classical Principal Components (CPC) and Chord-Transformed PC, respectively. The results demonstrate that the suggested method outperformed not only the CPC but also the previously presented state-of-the-art methods in the domain of DS face recognition.
Additional file 1.Three-dimensional raw data for each iteration state.
Additional file 2. PCs scores for each iteration state.
Additional file 2: Table S2. Raw three-dimensional digitized data for Bosphorus dataset expressi... more Additional file 2: Table S2. Raw three-dimensional digitized data for Bosphorus dataset expression group.
Additional file 1: Table S1. Raw three-dimensional digitized data for Stirling dataset expressio... more Additional file 1: Table S1. Raw three-dimensional digitized data for Stirling dataset expression group.
Additional file 4:. Table S4. PCs scores for all subjects used in the Bosphorus dataset
Additional file 3: Table S3. PCs scores for all subjects used in the Stirling dataset.
2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), 2019
Breast cancer is the leading cancer in the world. Mammogram is a gold standard for detecting brea... more Breast cancer is the leading cancer in the world. Mammogram is a gold standard for detecting breast cancer at earlier screening because of its sensitivity. Standard grayscale mammogram images are used by expert radiologists and Computer Aided-Diagnosis (CAD) systems. Yet, this original x-ray color provides little information to human radiologists and CAD systems to make decision. This binary color code thus affects sensitivity and specificity of prediction and subsequently affects accuracy. In order to enhance classifier models’ performance, this paper proposes a novel feature-level data integration method that combines features from grayscale mammogram and spectrum mammogram based on a deep neural network (DNN), called HARIRAYA. Pseudo-color is generated using spectrum color code to produce Spectrum mammogram from grayscale mammogram. The DNN is trained with three layers: grayscale, false-color and joint feature representation layers. Empirical results show that the multi-modal DNN...
2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS), 2019
Expression in H-sapiens plays a remarkable role when it comes to social communication. The identi... more 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. The results indicate that Fear expression has the lowest recognition accuracy while Surprise expression has the highest recognition accuracy. The classifier achieved a recognition accuracy of 99.58%.
2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC), 2019
Sexual dimorphism in Homo-sapiens is a phenomenon of a direct product of evolution by natural sel... more Sexual dimorphism in Homo-sapiens is a phenomenon of a direct product of evolution by natural selection where evolutionary forces acted separately on the sexes which brought about the differences in appearance between male and female such as in shape and size. This study investigates sexual dimorphism in human face with the application of Automatic Homologous Multi-points Warping (AHMW) for 3D facial landmark by building a template mesh as a reference object which is thereby applied to each of the target mesh on Stirling/ESRC dataset containing 101 subjects (male = 47, female = 54). The semi-landmarks are subjected to sliding along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal. Principal Component Analysis (PCA) is used for feature selection and the features are classified using Linear Discriminant Analysis (LDA) with an accuracy of 99.01%.
Making a gender comparison between male and female is not a difficult task for human beings but t... more Making a gender comparison between male and female is not a difficult task for human beings but the science of gender comparison of faces by humans is completely unfathomable due to commonality of gender comparison in both humans and other animal species. Significant gender differences between masculine and feminine exist in many facial regions such as eyes, nose, mouth, cheek and chin; which have not been critically looked into. This research characterizes and analyzes the gender comparison in the human face as a function of face features and identifies the features which contribute significantly to the uniqueness of the face using morphometrics techniques such as Principal Component Analysis (PCA), Thin-Plate Spline (TPS) Warping and Procrustes Superimposition (PS). The results demonstrate that the male face is significantly different from that of female based on the analysis of the selected facial features which provides the basis for gender-based comparison of faces.
Scientific Reports, 2021
Angelman syndrome (AS) is one of the common genetic disorders that could emerge either from a 15q... more Angelman syndrome (AS) is one of the common genetic disorders that could emerge either from a 15q11–q13 deletion or paternal uniparental disomy (UPD) or imprinting or UBE3A mutations. AS comes with various behavioral and phenotypic variability, but the acquisition of subjects for experiment and automating the landmarking process to characterize facial morphology for Angelman syndrome variation investigation are common challenges. By automatically detecting and annotating subject faces, we collected 83 landmarks and 10 anthropometric linear distances were measured from 17 selected anatomical landmarks to account for shape variability. Statistical analyses were performed on the extracted data to investigate facial variation in each age group. There is a correspondence in the results achieved by relative warp (RW) of the principal component (PC) and the thin-plate spline (TPS) interpolation. The group is highly discriminated and the pattern of shape variability is higher in children th...
PLOS ONE, 2020
Background The application of three-dimensional scan models offers a useful resource for studying... more Background The application of three-dimensional scan models offers a useful resource for studying craniofacial variation. The complex mathematical analysis for facial point acquisition in threedimensional models has made many craniofacial assessments laborious. Method This study investigates three-dimensional (3D) soft-tissue craniofacial variation, with relation to ethnicity, sex and age variables in British and Irish white Europeans. This utilizes a geometric morphometric approach on a subsampled dataset comprising 292 scans, taken from a Liverpool-York Head Model database. Shape variation and analysis of each variable are tested using 20 anchor anatomical landmarks and 480 sliding semi-landmarks. Results Significant ethnicity, sex, and age differences are observed for measurement covering major aspects of the craniofacial shape. The ethnicity shows subtle significant differences compared to sex and age; even though it presents the lowest classification accuracy. The magnitude of dimorphism in sex is revealed in the facial, nasal and crania measurement. Significant shape differences are also seen at each age group, with some distinct dimorphic features present in the age groups. Conclusions The patterns of shape variation show that white British individuals have a more rounded head shape, whereas white Irish individuals have a narrower head shape. White British persons also demonstrate higher classification accuracy. Regarding sex patterns, males are relatively larger than females, especially in the mouth and nasal regions. Females presented with higher classification accuracy than males. The differences in the chin, mouth, nose,
Symmetry, 2020
One of the most pertinent applications of image analysis is face recognition and one of the most ... more One of the most pertinent applications of image analysis is face recognition and one of the most common genetic disorders is Down syndrome (DS), which is caused by chromosome abnormalities in humans. It is currently a challenge in computer vision in the domain of DS face recognition to build an automated system that equals the human ability to recognize face as one of the symmetrical structures in the body. Consequently, the use of machine learning methods has facilitated the recognition of facial dysmorphic features associated with DS. This paper aims to present a concise review of DS face recognition using the currently published literature by following the generic face recognition pipeline (face detection, feature extraction, and classification) and to identify critical knowledge gaps and directions for future research. The technologies underlying facial analysis presented in recent studies have helped expert clinicians in general genetic disorders and DS prediction.
BMC Bioinformatics, 2020
Background Landmark-based approaches of two- or three-dimensional coordinates are the most widely... more Background Landmark-based approaches of two- or three-dimensional coordinates are the most widely used in geometric morphometrics (GM). As human face hosts the organs that act as the central interface for identification, more landmarks are needed to characterize biological shape variation. Because the use of few anatomical landmarks may not be sufficient for variability of some biological patterns and form, sliding semi-landmarks are required to quantify complex shape. Results This study investigates the effect of iterations in sliding semi-landmarks and their results on the predictive ability in GM analyses of soft-tissue in 3D human face. Principal Component Analysis (PCA) is used for feature selection and the gender are predicted using Linear Discriminant Analysis (LDA) to test the effect of each relaxation state. The results show that the classification accuracy is affected by the number of iterations but not in progressive pattern. Also, there is stability at 12 relaxation stat...
International Journal of Morphology, 2020
Sexual dimorphism in Homo-sapiens is a phenomenon of a direct product of evolution by natural sel... more Sexual dimorphism in Homo-sapiens is a phenomenon of a direct product of evolution by natural selection where evolutionary forces acted separately on the sexes which brought about the differences in appearance between male and female such as in shape and size. Advances in morphometrics have skyrocketed the rate of research on sex differences in human and other species. However, the current challenges facing 3D in the acquisition of facial data such as lack of homology, insufficient landmarks to characterize the facial shape and complex computational process for facial point digitization require further study in the domain of sex dimorphism. This study investigates sexual dimorphism in the human face with the application of Automatic Homologous Multi-points Warping (AHMW) for 3D facial landmark by building a template mesh as a reference object which is thereby applied to each of the target mesh on Stirling/ESRC dataset containing 101 subjects (male = 47, female = 54). The semi-landmarks are subjected to sliding along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal. Principal Component Analysis (PCA) is used for feature selection and the features are classified using Linear Discriminant Analysis (LDA) with an accuracy of 99.01 % which demonstrates that the method is robust.
BMC Bioinformatics, 2019
Background Expression in H-sapiens plays a remarkable role when it comes to social communication.... more 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 ...
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Papers by Olalekan Agbolade