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|Title:||Face detection and face recognition of human-like characters in comics|
|Authors:||Cheung, Savina Chui Shan (張翠姍)|
|Department:||Department of Computer Science|
|Supervisor:||Supervisor: Dr. Leung, Howard Wing Ho; First Reader: Dr. Ngo, Chong Wah; Second Reader: Prof. IP, Horace Ho Shing|
|Subjects:||Human face recognition (Computer science)|
Comic books, strips, etc.
|Description:||Nominated as OAPS (Outstanding Academic Papers by Students) paper by Department in 2007-08.|
|Abstract:||In a nutshell, it is inconvenient for comic readers to perform a scene search on large volumes of comic pages, as a conventional way to achieve the task is to perform brute force searching based on the vague impression of searchers. With the emergence of e-comics, computers could be designed to achieve the search task by comic characters indexing. The search of characters under different occasions will be helpful in identifying which scenes are the craved ones by narrowing down the scope from the large amount of digital comic pages in the database. To be able to differentiate between various cartoon characters for indexing, a content based image retrieval (CBIR) system is developed for the sake of comic readers. Under this project several detection and recognition strategies would be investigated to determine which algorithms, when being applied on e-comic data set, are more workable. After the comparison on the workable face detection and recognition algorithms were done from the literature, some of them have been culled to experiment on the comic data set. Overall 7 algorithms (3 for detection and 4 for recognition) are selected to work on the experiments, and the most workable methodologies are found to be Adaboost (detection) and Elastic Bunch Graph Matching [EBGM](recognition), yielding a rate of 45.50% and 54.44% respectively. To compensate for the imperfectness of the detection rate, the CBIR system developed are embedded with a modification function for users to add in undetected faces as for input in recognition; where to improve the recognition result, some knowledge from the comic nature are utilized as to boost the performance of EBGM, resulting an increase of 38.79% from the original recognition rate, the overall recognition first-rank rate is finalized as 75.50%. Although the performance is still not 100% accurate, the CBIR system might be able to search the specific scene if users provide more information to it. The CBIR system deployed is also designed in such a way that, if being used continuously, the performance of recognition will be enhanced.|
|Appears in Collections:||Computer Science - Undergraduate Final Year Projects|
OAPS - Dept. of Computer Science
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