Repo for the ACMMM20 submission: "Personalized breath based biometric authentication with wearable multimodality".

Last update: Nov 15, 2021

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Deep Learning personalized-breath

Overview

personalized-breath

Repo for the ACMMM20 submission: "Personalized breath based biometric authentication with wearable multimodality".

Guideline

To extract features:

cd personalized-breath/
python src/processing/extract_acce_gyro.py
python src/processing/extract_audio.py

To train model:

python src/main.py --breath_type=normal --model_type=multi --model_name=tcn --no_outer=0
results stored in results/

To get results:

with open('results/outputs/'+breath_type+'/list_test_acc_'+model_name, 'rb') as filehandle: list_test_acc = pickle.load(filehandle)

Owner

Manh-Ha Bui

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Repo for the ACMMM20 submission: "Personalized breath based biometric authentication with wearable multimodality".

Related tags

Overview

personalized-breath

Guideline

Owner

Manh-Ha Bui

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

Like a cowsay but without cows!

[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression

PyTorch Implementation for "ForkGAN with SIngle Rainy NIght Images: Leveraging the RumiGAN to See into the Rainy Night"

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Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.

Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP

K-FACE Analysis Project on Pytorch

Much faster than SORT(Simple Online and Realtime Tracking), a little worse than SORT

Official repo for our 3DV 2021 paper "Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements".

🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~

Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021)

Keras-1D-NN-Classifier

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

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