# Learning Deep and Compact Models for Gesture Recognition Source: [Learning Deep and Compact Models for Gesture Recognition]() Collection index: [llms.txt]() Data updated: 2025-12-25T02:36:28.873Z Authors: Koustav Mullick, Anoop M. Namboodiri Published: 2017-12-29T07:38:43.000Z ## Abstract We look at the problem of developing a compact and accurate model for gesture recognition from videos in a deep-learning framework. Towards this we propose a joint 3DCNN-LSTM model that is end-to-end trainable and is shown to be better suited to capture the dynamic information in actions. The solution achieves close to state-of-the-art accuracy on the ChaLearn dataset, with only half the model size. We also explore ways to derive a much more compact representation in a knowledge distillation framework followed by model compression. The final model is less than $1~MB$ in size, which is less than one hundredth of our initial model, with a drop of $7\\%$ in accuracy, and is suitable for real-time gesture recognition on mobile devices. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Gesture Recognition on ChaLearn Looking at People Challenge 2014 (test)](): Gesture Recognition; ChaLearn Looking at People Challenge 2014 (test); Accuracy: 93.2. ## Original sources and implementation [Original paper]() [PDF]()