Spiking Neural Network Enhanced Hand Gesture Recognition Using Low-Cost
  Single-photon Avalanche Diode Array
Zhenya Zang Xingda Li David Day Uei Li
We present a compact spiking convolutional neural network (SCNN) and spiking
multilayer perceptron (SMLP) to recognize ten different gestures in dark and
bright light environments, using a $9.6 single-photon avalanche diode (SPAD)
array. In our hand gesture recognition (HGR) system, photon intensity data was
leveraged to train and test the network. A vanilla convolutional neural network
(CNN) was also implemented to compare the performance of SCNN with the same
network topologies and training strategies. Our SCNN was trained from scratch
instead of being converted from the CNN. We tested the three models in dark and
ambient light (AL)-corrupted environments. The results indicate that SCNN
achieves comparable accuracy (90.8%) to CNN (92.9%) and exhibits lower floating
operations with only 8 timesteps. SMLP also presents a trade-off between
computational workload and accuracy. The code and collected datasets of this
work are available at https://github.com/zzy666666zzy/TinyLiDAR_NET_SNN.