# Gesture Recognition on ChaLearn Looking at People Challenge 2014 (test) Source: [Gesture Recognition on ChaLearn Looking at People Challenge 2014 (test)]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:27:16.640Z Evaluation of Gesture Recognition performance on the test split of the ChaLearn Looking at People Challenge 2014 dataset, typically measured using Accuracy. 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. | Scope | Recorded value | | --- | --- | | Task | Gesture Recognition | | Dataset (including any stated split) | ChaLearn Looking at People Challenge 2014 (test) | | Metric | Accuracy | | Unit | PERCENT | | Direction | Higher is better | | Results loaded | 6 of 6 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [Accuracy](): Higher is better; unit: PERCENT. ## Recorded results | Method | Accuracy | Result date | Report paper | | --- | --- | --- | --- | | ModDrop | 96.8 | Not recorded | 68fa63e91e209131cea015f5 | | 3D-CNN + LSTM | 93.2 | Not recorded | 68fa63e91e209131cea015f5 | | Pigou et al. | 91.4 | Not recorded | 68fa63e91e209131cea015f5 | | Baseline 3D-CNN | 90.1 | Not recorded | 68fa63e91e209131cea015f5 | | Deep Dynamic Neural Networks | 87.9 | Not recorded | 68fa63e91e209131cea015f5 | | Baseline LSTM | 86.6 | Not recorded | 68fa63e91e209131cea015f5 | ### ModDrop Accuracy: 96.8. Result ID: 698c067df729bc7f68e7e1f0. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Modality":"Multi-scale and multi-modal"}. This record does not assert independent reproduction. ### 3D-CNN + LSTM Accuracy: 93.2. Result ID: 698c067df729bc7f68e7e1ed. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction. ### Pigou et al. Accuracy: 91.4. Result ID: 698c067df729bc7f68e7e1ef. [Reporting paper]() Recorded evaluation configuration: {"Architecture":"3D-CNN based"}. This record does not assert independent reproduction. ### Baseline 3D-CNN Accuracy: 90.1. Result ID: 698c067df729bc7f68e7e1ec. [Reporting paper]() Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction. ### Deep Dynamic Neural Networks Accuracy: 87.9. Result ID: 698c067df729bc7f68e7e1ee. [Reporting paper]() Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction. ### Baseline LSTM Accuracy: 86.6. Result ID: 698c067df729bc7f68e7e1eb. [Reporting paper]() Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.