Prediction of Human Ethnicity from Facial Images Using Neural Networks

Masood, Sarfaraz; Gupta, Shubham; Wajid, Abdul; Gupta, Suhani; Ahmed, Musheer

doi:10.1007/978-981-10-3223-3_20

Cited by 31 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The next step would be identifying traits and contours. Masood et al [30] proposed a normalized forehead area calculation using the Sobel edge detection method [31]. The following prediction of human ethnicity study [30] acknowledges that some races, Black for instance, have higher forehead ratios.…”

Section: Ai Techniques For Identifying Controversial Featuresmentioning

confidence: 99%

AI and recruiting software: Ethical and legal implications

Fernández-Martínez

Fernández

2020

Paladyn, Journal of Behavioral Robotics

View full text Add to dashboard Cite

AbstractIn this article, we examine the state-of-the-art and current applications of artificial intelligence (AI), specifically for human resources (HR). We study whether, due to the experimental state of the algorithms used and the nature of training and test samples, a further control and auditing in the research community is necessary to guarantee fair and accurate results. In particular, we identify the positive and negative consequences of the usage of video-interview analysis via AI in recruiting processes as well as the main machine learning techniques used and their degrees of efficiency. We focus on some controversial characteristics that could lead to ethical and legal consequences for candidates, companies and states regarding discrimination in the job market (e.g. gender and race). There is a lack of regulation and a need for external and neutral auditing for the type of analyses done in interviews. We present a multi-agent architecture that aims at total legal compliance and more effective HR processes management.

show abstract

Section: Ai Techniques For Identifying Controversial Featuresmentioning

confidence: 99%

AI and recruiting software: Ethical and legal implications

Fernández-Martínez

Fernández

2020

Paladyn, Journal of Behavioral Robotics

View full text Add to dashboard Cite

show abstract

“…S. Masood et al [36], carried out two experiments in relation with the ethnicity classification problem. They, for their experiments, considered three ethnic groups namely-Mongoloid, Caucasoid and Negroid.…”

Section: Literature Surveymentioning

confidence: 99%

Racial Classification from Face

Ahmed*¹,

Choudhury²,

Borbora³

2019

IJRTE

View full text Add to dashboard Cite

Abstract: FACE is one of the major sources of social information like race, age, gender etc. At different levels of classification, prediction and identification face plays a major role, apart from other parts of the human body. As per literature Race is a form of classification for categorizing human beings in to groups based on geographic boundaries, physical appearances(including face), ethnicity and social status. In this paper we are trying to focus on different facial datasets those are currently available without any cost (but with licensing restrictions). Here, we are also representing our study of different works carried-out related with the racial classification and related topics.FERET. The two experiments were carried-out using ANN and CNN respectively. In the 1 st experiment, they (i) calculated Geometric Features, (ii) extracted Skin Color and (iii) calculated Normalized Forehead Area. For the network, they used, out of 447 images (from FERET), 320 were used for training, 37 for validation and 97 for testing. In the 2 nd experiment, they used a pre-trained model [37] for the extraction of features from the samples (both training and testing). The used Network consisted of 13 convolution layers. After performing the feature extraction, training and testing was performed. The result of the experiments showed superiority of CNN over ANN as the accuracies were 98.6% and 82.4% respectively irrespective of costs. It also showed that time taken for extraction of features and training of the network by CNN is more than ANN. VI. CONCLUSIONWe divided our survey in two parts: (i) about the different recognized facial datasets and the (ii) about different procedures and methods adopted by various researchers from feature selection to race and ethnicity classification. From the survey, it is noticed that the face can precisely give the race. The survey shows that in the last few years, lots of researches are carried out and which basically signifies a tremendous progress in the learning process of race from facial images. In this survey we tried to provides, more or less, all the existing facial image datasets and also a comprehensive review of the advances in race.

show abstract

“…In Computer Vision, human face and facial features gain a lot of attention from researchers and are considered as one of the most popular topics recently [2], [3]. Features and information extracted from a person such as age, gender, and ethnicity, are known as soft biometric.…”

Section: Introductionmentioning

confidence: 99%

A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach

And

Prince

2021

IEEE Access

View full text Add to dashboard Cite

Human face and facial features gain a lot of attention from researchers and are considered as one of the most popular topics recently. Features and information extracted from a person are known as soft biometric, they have been used to improve the recognition performance and enhance the search engine for face images, which can be further applied in various fields such as law enforcement, surveillance videos, advertisement, and social media profiling. By observing relevant studies in the field, we noted a lack of mention of the Arab world and an absence of Arab dataset as well. Therefore, our aim in this paper is to create an Arab dataset with proper labeling of Arab sub-ethnic groups, then classify these labels using deep learning approaches. Arab image dataset that was created consists of three labels: Gulf Cooperation Council countries (GCC), the Levant, and Egyptian. Two types of learning were used to solve the problem. The first type is supervised deep learning (classification); a Convolutional Neural Network (CNN) pre-trained model has been used as CNN models achieved state of art results in computer vision classification problems. The second type is unsupervised deep learning (deep clustering). The aim of using unsupervised learning is to explore the ability of such models in classifying ethnicities. To our knowledge, this is the first time deep clustering is used for ethnicity classification problems. For this, three methods were chosen. The best result of training a pre-trained CNN on the full Arab dataset then evaluating on a different dataset was 56.97%, and 52.12% when Arab dataset labels were balanced. The methods of deep clustering were applied on different datasets, showed an ACC from 32% to 59%, and NMI and ARI result from zero to 0.2714 and 0.2543 respectively.

show abstract

Prediction of Human Ethnicity from Facial Images Using Neural Networks

Cited by 31 publications

References 11 publications

AI and recruiting software: Ethical and legal implications

AI and recruiting software: Ethical and legal implications

Racial Classification from Face

A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach

Contact Info

Product

Resources

About