Name
Title | ||
|---|---|---|
On Input/Output Architectures for Convolutional Neural Network-Based Cross-View Gait Recognition |
Abstract | ||
|---|---|---|
In this paper, we discuss input/output architectures for convolutional neural network (CNN)- based cross-view gait recognition. For this purpose, we consider two aspects: verification versus identifi- cation, and the trade-off between spatial displacement caused by subject difference and view difference. More specifically, we use the Siamese network with a pair of inputs and contrastive loss for verification, and a triplet network with a triplet of inputs and triplet ranking loss for identification. The aforementioned CNN architec- tures are insensitive to spatial displacement because the difference between a matching pair is calculated at the last layer after passing through the convolution and max pooling layers; hence, they are expected to work relatively well under large view differences. By contrast, because it is better to use the spatial displace- ment to its best advantage because of the subject dif- ference under small view differences, we also use CNN architectures where the difference between a matching pair is calculated at the input level to make them more sensitive to spatial displacement. We conducted experiments for cross-view gait recognition and con- firmed that the proposed architectures outperformed the state-of-the-art benchmarks in accordance with their suitable situations of verification/identification tasks and view differences. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCSVT.2017.2760835 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | Field | DocType |
Gait recognition,Probes,Network architecture,Robustness,Performance evaluation,Neural networks | Computer vision,Pattern recognition,Ranking,Convolutional neural network,Computer science,Convolution,Pooling,Network architecture,Input/output,Robustness (computer science),Artificial intelligence,Artificial neural network | Journal |
Volume | Issue | ISSN |
29 | 9 | 1051-8215 |
Citations | PageRank | References |
13 | 0.49 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noriko Takemura | 1 | 30 | 6.58 |
| Yasushi Makihara | 2 | 1012 | 70.67 |
| Daigo Muramatsu | 3 | 262 | 24.88 |
| Tomio Echigo | 4 | 348 | 25.41 |
| Yasushi Yagi | 5 | 1752 | 186.22 |