Name
Title | ||
|---|---|---|
An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks. |
Abstract | ||
|---|---|---|
This research study proposes a novel method of inter-related problems in face recognition using the NeuCube neuromorphic computational platform. We investigated age classification and gender recognition. The well-known FG-NET and MORPH Album 2 image gallery were used and anthropometric features were extracted from landmark points on the face. The landmarks were pre-processed with the procrustes algorithm before feature extraction was performed. The Weka machine learning workbench was used to compare the performance of traditional techniques such as the K nearest neighbor (Knn) and Multi-LayerPerceptron (MLP) with NeuCube. Our empirical results show that NeuCube performed consistently better across both problem types that we investigated. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s12530-017-9175-y | Evolving Systems |
Keywords | Field | DocType |
Anthropometric model, Age group classification, Gender classification, Spiking neural networks | k-nearest neighbors algorithm,Workbench,Facial recognition system,Pattern recognition,Computer science,Neuromorphic engineering,Feature extraction,Artificial intelligence,Landmark,Spiking neural network,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 2 | 1868-6486 |
Citations | PageRank | References |
2 | 0.36 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fahad Bashir Alvi | 1 | 41 | 2.24 |
| Russel Pears | 2 | 205 | 27.00 |
| Nikola K Kasabov | 3 | 3645 | 290.73 |