| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| model_1_nimish.tflearn.data-00000-of-00001 | 2019-08-21 | 455.1 MB | |
| neutral.png | 2019-08-21 | 12.1 kB | |
| sad.png | 2019-08-21 | 16.4 kB | |
| surprised.png | 2019-08-21 | 14.7 kB | |
| fearful.png | 2019-08-21 | 16.2 kB | |
| happy.png | 2019-08-21 | 16.6 kB | |
| angry.png | 2019-08-21 | 14.6 kB | |
| disgusted.png | 2019-08-21 | 16.2 kB | |
| abc | 2019-08-21 | 1 Byte | |
| trained-model.txt | 2019-08-21 | 331 Bytes | |
| haarcascade_frontalface_default.xml | 2019-08-21 | 963.4 kB | |
| run.cpython-36.pyc | 2019-08-21 | 2.2 kB | |
| em_model.cpython-36.pyc | 2019-08-21 | 2.4 kB | |
| model_1_nimish.tflearn.meta | 2019-08-21 | 168.5 kB | |
| model_1_nimish.tflearn.index | 2019-08-21 | 1.1 kB | |
| em_model.py | 2019-08-21 | 2.1 kB | |
| version.py | 2019-08-21 | 46 Bytes | |
| LICENSE | 2019-08-21 | 11.4 kB | |
| run.py | 2019-08-21 | 2.2 kB | |
| 92bbf76de2f42487366b59f1b5b50cf4d60f29 | 2019-08-21 | 183 Bytes | |
| e4784429db15a7b874ef99dbc9afc7f655879e | 2019-08-21 | 187 Bytes | |
| 4ba7a2bcb3fe32a4ce316300f23a16182d170e | 2019-08-21 | 190 Bytes | |
| b5a8ea0a81abd20841a2fbd20caad01f37bb79 | 2019-08-21 | 183 Bytes | |
| 1b55fb5a651a573a9990fdd8457851fa5ecc42 | 2019-08-21 | 189 Bytes | |
| 560c4640801c028fd62b45cdf5e9c9689e8f9d | 2019-08-21 | 155 Bytes | |
| 8f9f73a0fe1a2d8521348d812ea026cb1ab96c | 2019-08-21 | 189 Bytes | |
| bc69d8b01f19b0a84dbc0d7cd608a5780ffb05 | 2019-08-21 | 16.4 kB | |
| 1cfe8e9a48689d7949bb8671b0bc17ce91ba58 | 2019-08-21 | 550 Bytes | |
| d1e7633dc025740b08182c4746b70d98db55d5 | 2019-08-21 | 190 Bytes | |
| 618b883c2ab7d89e827a5ac26fffa1d1bf6a10 | 2019-08-21 | 550 Bytes | |
| 20ab7683a5b2845ba8a87acb8748e65cb14402 | 2019-08-21 | 561 Bytes | |
| 38136e0acf173073a74344a90d2e9a21b61e4d | 2019-08-21 | 255 Bytes | |
| 4ecc35d351837d0429874c0644189c62f5251b | 2019-08-21 | 565 Bytes | |
| master | 2019-08-21 | 41 Bytes | |
| prepare-commit-msg.sample | 2019-08-21 | 1.5 kB | |
| update.sample | 2019-08-21 | 3.6 kB | |
| commit-msg.sample | 2019-08-21 | 896 Bytes | |
| pre-applypatch.sample | 2019-08-21 | 424 Bytes | |
| pre-push.sample | 2019-08-21 | 1.3 kB | |
| applypatch-msg.sample | 2019-08-21 | 478 Bytes | |
| fsmonitor-watchman.sample | 2019-08-21 | 3.3 kB | |
| post-update.sample | 2019-08-21 | 189 Bytes | |
| pre-commit.sample | 2019-08-21 | 1.6 kB | |
| pre-rebase.sample | 2019-08-21 | 4.9 kB | |
| pre-receive.sample | 2019-08-21 | 544 Bytes | |
| exclude | 2019-08-21 | 240 Bytes | |
| HEAD | 2019-08-21 | 215 Bytes | |
| config | 2019-08-21 | 293 Bytes | |
| index | 2019-08-21 | 1.6 kB | |
| packed-refs | 2019-08-21 | 189 Bytes | |
| description | 2019-08-21 | 73 Bytes | |
| abc.txt | 2019-08-21 | 1 Byte | |
| README.md | 2019-08-21 | 723 Bytes | |
| Totals: 53 Items | 456.4 MB | 0 |
Emotion-recognition-and-prediction
A real time system to detect human emotions from image and voice and predict its reaction. This is a pre-trained model with an accuracy of 68.7% over Facial Expression Recognition (FER) 2013 dataset. This project is the direct outcome of my graduation submission at MIT, Pune. The extraction of emotions from audio is done by using pyAudio Reaction prediction module is under development and is not included yet
Prerequisites
Python 3
OpenCV
Installation
pip install tensorflow numpy scipy h5py
Download the trained model from the links given in source/trained-model.txt
Testing
python em_model.py
