On the Ethnic Classification of Pakistani Face using Deep Learning

Jilani, Shelina Khalid; Ugail, Hassan; Bukar, Ali Maina; Logan, Andrew

doi:10.1109/cw.2019.00039

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Seven of the 18 studies in this review use anthropometric indices as outcome measures. However, standardisation of anthropometric methods is needed to facilitate direct comparison (51). In addition, studies used North American White (NAW) data to draw conclusions despite demographic variations in their cohorts.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“Should Pediatric Septal Surgery and Septorhinoplasty Be Performed for Nasal Obstruction?”—A Systematic Review of the Literature

Howard,

Williams,

Navaratnam

et al. 2023

Facial Plast Surg

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Corrective septal surgery for children with nasal obstruction has historically been avoided due to concern about the impact on the growing nose, with disruption of midfacial growth. However, there is a paucity of data evaluating complication and revision rates post nasal septal surgery in the paediatric population. In addition, there is evidence to suggest that failure to treat nasal obstruction in children may itself result in facial deformity and/or developmental delay. The aim of this systematic review is to evaluate the efficacy and safety of septal surgery in paediatric patients with nasal obstruction. A systematic review was conducted in accordance with PRISMA guidelines. MEDLINE, Embase and the Cochrane Library were searched. Original studies in paediatric patients (<18 years of age) with nasal obstruction were eligible for inclusion. Patients with cleft lip or palate as their primary diagnosis were excluded. Our primary outcomes were patient reported outcome measures (PROMs), postsurgical complications and revision rates. Secondary outcomes included surgical technique, anatomical considerations and anthropometric measurements. 18 studies were included (1080 patients). Patients underwent, septoplasty, septorhinoplasty, rhinoplasty, or a combination of procedures, for nasal obstruction. Obstruction was commonly reported secondary to trauma, nasal septal deviation or congenital deformity. Mean age of patients was 13.04 with an average follow-up of 41.8 months. 5.6% patients required revision surgery and there was an overall complication rate of 7.8%. Septal surgery for nasal obstruction in children has low revision and complication rates. However, a paediatric-specific outcome measure is yet to be determined. Larger prospective studies with long-term follow-up periods are needed to determine the optimal timing of nasal surgery for nasal obstruction in the paediatric population.

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Section: Accepted Manuscriptmentioning

confidence: 99%

“Should Pediatric Septal Surgery and Septorhinoplasty Be Performed for Nasal Obstruction?”—A Systematic Review of the Literature

Howard,

Williams,

Navaratnam

et al. 2023

Facial Plast Surg

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“…Deep learning has been applied in the medical field to address various problems such as face recognition [5][6][7], effective classification of skin burns [8][9][10][11][12], and cancer diagnosis [13][14][15], as well as in financial fraud detection [16,17]. Interestingly, a similar approach was adopted recently to discriminate between blood-smear images that include the Plasmodium parasite and those that do not.…”

Section: Literature Reviewmentioning

confidence: 99%

DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

Abubakar

Ajuji

Yahya

2021

ASI

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Malaria is one of the most infectious diseases in the world, particularly in developing continents such as Africa and Asia. Due to the high number of cases and lack of sufficient diagnostic facilities and experienced medical personnel, there is a need for advanced diagnostic procedures to complement existing methods. For this reason, this study proposes the use of machine-learning models to detect the malaria parasite in blood-smear images. Six different features—VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 models—were extracted. Then Decision Tree, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour classifiers were trained using these six features. Extensive performance analysis is presented in terms of precision, recall, f-1score, accuracy, and computational time. The results showed that automating the process can effectively detect the malaria parasite in blood samples with an accuracy of over 94% with less complexity than the previous approaches found in the literature.

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“…Table 1 summarizes the data sets and the statistical tests of the discussed anthropometrical measurements-based classification studies. It is worthwhile to mention that in contrast to the anthropometrical measurements-based classification scheme, appearance-based classification schemes that utilize machine learning [10]- [13] or even deep learning paradigm [14]- [16] exists, and have also obtained success in ethnicity, sex and age estimation studies. Deep learning offers a radical alter- VOLUME 4, 2016 native to traditional feature-based approaches as it performs automatic feature extraction on the facial images to obtain learned features.…”

Section: Introductionmentioning

confidence: 99%

Facial Anthropometric Measurements and Photographs — An Interdisciplinary Study

et al. 2020

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In recent years, automatic facial analysis has attracted much interest among computer science researchers in the healthcare and computer vision fields studying facial anthropometric measurements using photographs. However, to date, there have been no healthcare or computer vision publications that use standardized photographs to differentiate features between sub-ethnic groups by leveraging the power of machine learning on two-dimensional computer vision benchmark data sets (2D CVBDs). Thus, the present work is an interdisciplinary study at the interface of healthcare and computer vision fields that attempts to fill this literature gap where we explore the use of machine learning on 2,789 photographs from eleven 2D CVBDs to identify k top discriminative features in major and sub-ethnic groups. These features are ranked based on information gain values and p-values. We also provide a comprehensive analysis of using information-gain-based and p-value-based features. Our machine learning model achieves an accuracy of 96-99%, and our findings reveal that information-gain-based features have the upper hand over p-valuebased features. The top three information-gain-based features in sub-ethnic groups are: dn (distance from the tip of the nose to the center of the mouth), hf (face height) and wn (nose width), while the top three information-gain-based features in major ethnic groups are: de (distance between the inner corners of the eyelids), hf and dn. These results are then compared to the results obtained using standard deep learning techniques such as OxfordNet (VGG16), Residual Networks (ResNet50), and Inception-V3, where accuracy of 90-94% was seen. We hope that these findings will lead to future collaboration between computer vision and healthcare researchers studying facial anthropometric measurement studies.

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On the Ethnic Classification of Pakistani Face using Deep Learning

Cited by 6 publications

References 32 publications

“Should Pediatric Septal Surgery and Septorhinoplasty Be Performed for Nasal Obstruction?”—A Systematic Review of the Literature

“Should Pediatric Septal Surgery and Septorhinoplasty Be Performed for Nasal Obstruction?”—A Systematic Review of the Literature

DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

Facial Anthropometric Measurements and Photographs — An Interdisciplinary Study

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