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 preprocessed 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 neighbour (Knn) and Multi-Layer Perceptron (MLP) with NeuCube. Our empirical results show that NeuCube performed consistently better across both problem types that we investigated.
- Anthropometric model
- Age grpoup classification
- Geneder classification
- Spiking neural networks
Alvi, F., Pears, R., & Kasabov, N. (2018). An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks. Evolving Systems, 9((2018)), 145-156. https://doi.org/10.1007/s12530-017-9175-y