| 1 |
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Facial feature discovery for ethnicity recognition
|
|
|
|
Abstract:
The salient facial feature discovery is one of the important research tasks in ethnical group face recognition. In this paper, we first construct an ethnical group face dataset including Chinese Uyghur, Tibetan, and Korean. Then, we show that the effective sparse sensing approach to general face recognition is not working anymore for ethnical group facial recognition if the features based on whole face image are used. This is partially due to a fact that each ethnical group may have its own characteristics manifesting only in specified face regions. Therefore, we will analyze the particularity of three ethnical groups and aim to find the common characterizations in some local regions for the three ethnical groups. For this purpose, we first use the facial landmark detector STASM to find some important landmarks in a face image, then, we use the well-known data mining technique, the mRMR algorithm, to select the salient geometric length features based on all possible lines connected by any two landmarks. Second, based on these selected salient features, we construct three “T” regions in a face image for ethnical feature representation and prove them to be effective areas for ethnicity recognition. Finally, some extensive experiments are conducted and the results reveal that the proposed “T” regions with extracted features are quite effective for ethnical group facial recognition when the L2-norm is adopted using the sparse sensing approach. In comparison to face recognition, the proposed three “T” regions are evaluated on the olivetti research laboratory face dataset, and the results show that the constructed “T” regions for ethnicity recognition are not suitable for general face recognition. This article is categorized under: Algorithmic Development > Structure Discovery Algorithmic Development > Biological Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Classification.
|
|
URL: https://doi.org/10.1002/widm.1278
http://hdl.handle.net/20.500.11937/71484
|
|
BASE
|
|
Hide details
|
|
|
| 4 |
Measuring web service security in the era of Internet of Things
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Multi-ethnic facial features extraction based on axiomatic fuzzy set theory
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Национальные ценности в русской и китайской лингвокультурах : магистерская диссертация ; National values in the Russian and Chinese liguistic cultures
|
|
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Classifying questions into fine-grained categories using topic enriching
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
The Twitter of Babel: Mapping World Languages through Microblogging Platforms
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Developmental dyslexia in Chinese and English populations: dissociating the effect of dyslexia from language differences
|
|
|
|
In: Brain , 133 (6) 1694 -1706. (2010) (2010)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Hong Kong People’s Attitudes Towards Varieties of English
|
|
|
|
In: Newcastle Working Papers in Linguistics, 2009 (2009)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
40th EASD Annual Meeting of the European Association for the Study of Diabetes : Munich, Germany, 5-9 September 2004.
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
