Name
Abstract | ||
|---|---|---|
Gender recognition has applications in human-computer interaction, biometric authentication, and targeted marketing. This paper presents an implementation of an algorithm for binary male/female gender recognition from face images based on a shunting inhibitory convolutional neural network, which has a reported accuracy on the FERET database of 97.2 %. The proposed hardware/software co-design approach using an ARM processor and FPGA can be used as an embedded system for a targeted marketing application to allow real-time processing. A threefold speedup is achieved in the presented approach compared to a software implementation on the ARM processor alone. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-42007-3_47 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Real-time,Embedded system,Computer vision,FPGA,Neural network,Co-design,Hardware acceleration | ARM architecture,Convolutional neural network,Computer science,Field-programmable gate array,Software,Hardware acceleration,FERET database,Hardware architecture,Speedup,Embedded system | Conference |
Volume | ISSN | Citations |
9799 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Y. Chen | 1 | 25 | 7.15 |
| Morteza Biglari-Abhari | 2 | 100 | 19.47 |
| Kevin I-Kai Wang | 3 | 167 | 29.65 |
| Abdesselam Bouzerdoum | 4 | 883 | 89.51 |
| Fok Hing Chi Tivive | 5 | 156 | 15.77 |