A Machine Learning Approach for Ethnic Classification: The British Pakistani Face

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

doi:10.1109/cw.2017.27

Cited by 13 publications

(11 citation statements)

References 16 publications

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“…The techniques used for feature estimation are of versatile nature for this trio like image processing based [4] to wavelets etc. Some commercial applications [108] and PCA [99] etc. are used for classification or retrieval.…”

Section: Techniques For Improved Global Traits Based Recognitionmentioning

confidence: 99%

Soft biometrics: a survey

Hassan

Izquierdo

Piatrik

2021

Multimed Tools Appl

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The field of biometrics research encompasses the need to associate an identity to an individual based on the persons physiological or behaviour traits. While the use of intrusive techniques such as retina scans and finger print identification has resulted in highly accurate systems, the scalability of such systems in real-world applications such as surveillance and border security has been limited. As a branch of biometrics research, the origin of soft biometrics could be traced back to need for non-intrusive solutions for extracting physiological traits of a person. Following high number of research outcomes reported in the literature on soft biometrics, this paper aims to consolidate the scope of soft biometrics research across four thematic schemes (i) a detailed review of soft biometrics research data sets, their annotation strategies and building a largest novel collection of soft traits; (ii) the assessment of metrics that affect the performance of soft biometrics system; (iii) a comparative analysis on feature and modality level fusion reported in the literature for enhancing the system performance; and (iv) a performance analysis of hybrid soft biometrics recognition system using multi-scale criterion. The paper also presents a detailed analysis on the global traits associated to person identity such as gender, age and ethnicity. The contribution of the paper is to provide a comprehensive review of scientific literature, identify open challenges and offer insights on new research directions in the filed.

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Section: Techniques For Improved Global Traits Based Recognitionmentioning

confidence: 99%

Soft biometrics: a survey

Hassan

Izquierdo

Piatrik

2021

Multimed Tools Appl

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“…The use of Convolutional Neural Networks (CNN) for classification tasks has widely been adopted in different application domains such as face recognition [13,14] and disease detection [15]. Their adoption was due to their capability to capture rich generic discriminatory features at different levels.…”

Section: Literaturementioning

confidence: 99%

Burns Depth Assessment Using Deep Learning Features

et al. 2020

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Purpose Burns depth evaluation is a lifesaving task and very challenging that requires objective techniques to accomplish. While the visual assessment is the most commonly used by surgeons, its accuracy reliability ranges between 60 and 80% and subjective that lacks any standard guideline. Currently, the only standard adjunct to clinical evaluation of burn depth is Laser Doppler Imaging (LDI) which measures microcirculation within the dermal tissue, providing the burns potential healing time which correspond to the depth of the injury achieving up to 100% accuracy. However, the use of LDI is limited due to many factors including high affordability and diagnostic costs, its accuracy is affected by movement which makes it difficult to assess paediatric patients, high level of human expertise is required to operate the device, and 100% accuracy possible after 72 h. These shortfalls necessitate the need for objective and affordable technique. Method In this study, we leverage the use of deep transfer learning technique using two pretrained models ResNet50 and VGG16 for the extraction of image patterns (ResFeat50 and VggFeat16) from a a burn dataset of 2080 RGB images which composed of healthy skin, first degree, second degree and third-degree burns evenly distributed. We then use One-versus-One Support Vector Machines (SVM) for multi-class prediction and was trained using 10-folds cross validation to achieve optimum trade-off between bias and variance. Results The proposed approach yields maximum prediction accuracy of 95.43% using ResFeat50 and 85.67% using VggFeat16. The average recall, precision and F1-score are 95.50%, 95.50%, 95.50% and 85.75%, 86.25%, 85.75% for both ResFeat50 and VggFeat16 respectively. Conclusion The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.

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“…It is evident that data surrounding the South Asian race is limited. Further, ethnicity verification for facial images of Pakistani origin, remains under-investigated with a single exception (Jilani et.al [14]).…”

Section: Guo and Mumentioning

confidence: 99%

“…The classifier is learned such that, , denotes facial images labelled as Pakistani and denotes Non-Pakistani images. Thus, using a Support Vector Machine algorithm, an optimum separating hyperplane was computed, which separated the classes into the two categories [14]. A -fold cross validation technique was employed to evaluate the performance of the pre-trained models.…”

Section: Classificationmentioning

confidence: 99%

On the Ethnic Classification of Pakistani Face using Deep Learning

Jilani

Ugail

Bukar

et al. 2019

2019 International Conference on Cyberworlds (CW)

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Demographic-based identification plays an active role in the field of face identification. Over the past decade, machine learning algorithms have been used to investigate challenges surrouding ethnic classification for specific populations, such as African, Asian and Caucasian people. Ethnic classification for individuals of South Asian, Pakistani heritage, however, remains to be addressed.The present paper addresses a two-category (Pakistani Vs Non-Pakistani) classification task from a novel, purpose-built dataset.To the best of our knowledge, this work is the first to report a machine learning ethnic classification task with South Asian (Pakistani) faces. We conduted a series of experiments using deep learning algorithms (ResNet-50, ResNet-101 and ResNet-152) for feature extraction and a linear support vector machine (SVM) for classification. The experimental results demonstrate ResNet-101 achieves the highest performance accuracy of 99.2% for full-face ethnicity classification, followed closely by 91.7% and 95.7% for the nose and mouth respectively.

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A Machine Learning Approach for Ethnic Classification: The British Pakistani Face

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Cited by 13 publications

References 16 publications

Soft biometrics: a survey

Soft biometrics: a survey

Burns Depth Assessment Using Deep Learning Features

On the Ethnic Classification of Pakistani Face using Deep Learning

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