Anime character face detection and recognition
Intelligence (AI) applications have grabbed headlines with their potential to radically improve human life and even transform the industry. Currently, in the field of pursuing detection and recognition of objects, human face recognition is a welldeserved representative and has reached the highest...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/4660/1/fyp_CS_2022_NHC.pdf http://eprints.utar.edu.my/4660/ |
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| Institution: | Universiti Tunku Abdul Rahman |
| Summary: | Intelligence (AI) applications have grabbed headlines with their potential to
radically improve human life and even transform the industry. Currently, in the field
of pursuing detection and recognition of objects, human face recognition is a welldeserved
representative and has reached the highest level. Nevertheless, the use of AI
in the field of art is still very few. The only famous ones nowadays are the appraisal
of paintings and recognition of painting style. Let alone facial recognition in the field
of art, such as the domain of this project, anime character face. There is still no
mature technology or well-known application for recognising anime faces. Even the
Google Lens, which claims to be able to search for all objects in the world, cannot
identify an anime character with half accuracy. Thus, there is an acute need for such a
system to detect and recognise anime character faces.
Therefore, in this project, an AI mobile app named “Gease” with the main
functionality of detecting and recognising multiple anime character’s faces are
developed. The mobile app will be the first mobile app specifically for anime
character face detection and recognition. Likewise, the recognisable number of
characters is up to 205 which are more than doubled compared to most of the existing
anime face detection and recognition projects which only involve at most 100
characters. The accuracy achieved 97% and 63.2% Top-1 accuracy for detection and
recognition respectively. While the inference time is 31.9ms and 4.8ms per face
detection and recognition respectively. Moreover, the FastAPI web framework that
supports Python development was utilized in this project. FastAPI allows the app
developed to be opened to external connections as a REST API and return the
detection and recognition results in JSON form which greatly flexes and expands the
value of the app.
Furthermore, this project developed an exquisite and smooth user interface while
having multi-platform adaptability. The mobile is a portable PWA which flourishing
recently. It accommodated multiple platforms including web browser, Android and
iOS with only one set of codes but maintains most of the functions of native apps. The
Ionic framework was used in design a cross-platform mobile user interface. |
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