Abstract
In the field of face biometrics, finding the identity of a person in an image is most researched, but there are other, soft biometric information that are equally as important, such as age, gender, ethnicity or emotion. Nowadays, ethnicity classification has a wide application area and is a prolific area of research. This paper gives an overview of recent advances in ethnicity classification with focus on convolutional neural networks (CNNs) and proposes a new ethnicity classification method using only the middle part of the face and CNN. The paper also compares the differences in results of CNN with and without plotted landmarks. The proposed model was tested using holdout testing method on UTKFace dataset and FairFace dataset. The accuracy of the model was 80.34% for classification into five classes and 61.74% for classification into seven classes, which is slightly better than state-of-the-art, but it is also important to note that results in this paper are obtained by using only the middle part of the face which reduces the time and resources necessary.
Highlights
Nowadays, face recognition has become almost inevitable in real-world applications in the fields of security, biometrics, entertainment industry and others in order to identify a person
The specific goal of this paper is to provide an overview of recent advances in ethnicity classification with focus on convolutional neural networks and to propose a new ethnicity classification method using only the middle part of the face, while employing convolutional neural networks and comparing differences in results of CNN with and without plotted landmarks
This paper focuses on CNNs for ethnicity classification
Summary
Face recognition has become almost inevitable in real-world applications in the fields of security, biometrics, entertainment industry and others in order to identify a person. Other than the identity, there are other, soft biometric traits that can be learned from faces such as age, gender, ethnicity or emotions. Soft biometrics has become one of the more prolific fields of research. Soft biometric traits are “physical, behavioural, or material accessories, which are associated with an individual, and which can be useful for recognising an individual. These attributes are typically gleaned from primary biometric data, are classifiable in pre-defined human understandable categories, and can be extracted in an automated manner” [2]. The soft biometric trait this paper focuses on is the ethnicity (race) of a person. Ethnicity classification has an important role in classifying face images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
