Recognizing Faces with Normalized Local Gabor Features and Spiking Neuron Patterns
We use Spike Response Model to reduce the dimension of local Gabor features into Spiking Neuron Patterns (SNP). The reduced Gabor features called LGFV//LN//SNP are classified using soft kNN classifier. SNP projection is fully unsupervised, does not require projection vector or neighborhood embedding and is more efficient than the original local Gabor features representation. SNP improves the face recognition accuracy of local Gabor features. The results show that our method is robust against expression, illumination, partial occlusions, age and random noise.
spiking neurons feature representation face recognition gabor wavelets dimensionality reduction