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Anthony V. W. Smith

dblp:01/3295 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1996
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Hardware accelerators and domain-specific architectures · 100%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Hardware accelerators and domain-specific architectures › machine learning accelerator
neural network accelerator		 0.011987
Bit-Serial Neural Networks · NIPS 1987
Machine learning › Deep learning architectures and training
neural network hardware		 0.011987
Bit-Serial Neural Networks · NIPS 1987

Methods — techniques the papers use, named apart from their topics

bit-serial computation · 0.0
YearPublicationVenuePosition
1996 Real-time facial expression recognition based on features' positions and dimensions
abstract
This paper describes a method of real-time facial expression recognition which is based on automatic measurement of the facial features' dimension and the positional relationship between them. The method is composed of two parts, the facial feature extraction using matching techniques and the facial expression recognition using statistics of position and dimension of the features. The method is implemented in an experimental hardware system and the performance is evaluated. The extraction rates of the facial-area, the mouth and the eyes are about 100%, 96% and 90%, respectively, and the recognition rates of facial expression such as normal, angry, surprise, smile and sad expression are 54%, 89%, 86%, 53% and 71%, respectively, for a specific person. The whole processing speed is about 15 frames/second. Finally, we touch on some applications such as man-machine interface, automatic generation of facial graphic animation and sign language translation using facial expression recognition techniques.
Histoshi Sako, Anthony V. W. Smith
 ICPR2
1987 Bit-Serial Neural Networks
Alan F. Murray, Anthony V. W. Smith, Zoe F. Butler
 NIPS2