In this paper, we propose a convolution neural network for classifying grayscale images of hand gestures. For classification, we look at ten different hand gestures collected from various people using a thermal camera. We then compare the proposed model's performance in terms of classification accuracy and inference time to that of other benchmark models. We demonstrate through extensive results that the proposed model achieves higher classification accuracy while using a smaller model size. Furthermore, we show that the proposed model outperforms benchmark models in terms of inference time.
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