Convolutional spiking neural network model for robust face detection

We propose a convolutional spiking neural network (CSNN) model with population coding for robust face detection. The basic structure of the network includes hierarchically alternating layers for feature detection and feature pooling. The proposed model implements hierarchical template matching by temporal integration of structured pulse packet. The packet signal represents some intermediate or complex visual feature (e.g., a pair of line segments, corners, eye, nose, etc.) that constitutes a face model. The output pulse of a feature pooling neuron represents some local feature (e.g., line segments). Introducing a population coding scheme in the CSNN architecture, we show how the biologically inspired model attains invariance to changes in size and position of face and ensures the efficiency of face detection.