trimodal_person_verification

This repository contains the code, and preprocessed dataset featured in "A Study of Multimodal Person Verification Using Audio-Visual-Thermal Data".

Person verification is the general task of verifying person’s identity using various biometric characteristics. We study an approach to multimodal person verification using audio, visual, and thermal modalities. In particular, we implemented unimodal, bimodal, and trimodal verification systems using the state-of-the-art deep learning architectures and compared their performance under clean and noisy conditions.

Dependencies

pip install -r requirements.txt

Dataset

In this work, we utilized the SpeakingFaces dataset to train, validate, and test the person verification systems. SpeakingFaces is a publicly available multimodal dataset comprised of audio, visual, and thermal data streams. The preprocessed data used for our experiments can be downloaded from Google Drive.

The data directory contains the compressed version of the preprocessed data used for the reported experiments. For each utterance, only the first frame (visual and thermal) is selected. The train set is split into 5 parts that should be extracted into the same location.

The data/metadata subdirectory contains lists prepared for the train, validation, and test sets following the format of VoxCeleb. In particular, the train list contains the paths to the recordings and the corresponding subject identifiers. The validation and test lists consist of randomly generated positive and negative pairs. For each subject, the same number of positive and negative pairs were selected. In total, the numbers of pairs in the validation and test sets are 38,000 and 46,200, respectively.

Note, to run noisy training and evaluation, you should first download the MUSAN dataset.

See trainSpeakerNet.py for details on where the data should be stored.

Training examples : clean data

Unimodal models

python trainSpeakerNet.py --model ResNetSE34Multi --modality wav --log_input True --trainfunc angleproto --max_epoch 1500 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.01 --seed 1 --save_path exps/wav/exp1

python trainSpeakerNet.py --model ResNetSE34Multi --modality rgb --log_input True --trainfunc angleproto --max_epoch 600 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.01 --seed 1 --save_path exps/rgb/exp1

python trainSpeakerNet.py --model ResNetSE34Multi --modality thr --log_input True --trainfunc angleproto --max_epoch 600 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.01 --seed 1 --save_path exps/thr/exp1

Multimodal models

python trainSpeakerNet.py --model ResNetSE34Multi --modality wavrgb --log_input True --trainfunc angleproto --max_epoch 600 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.01 --seed 1 --save_path exps/wavrgb/exp1

python trainSpeakerNet.py --model ResNetSE34Multi --modality wavrgbthr --log_input True --trainfunc angleproto --max_epoch 600 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.1 --seed 1 --save_path exps/wavrgb/exp1

Training examples : noisy data

Unimodal models

python trainSpeakerNet.py --model ResNetSE34Multi --modality wav --noisy_train True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --max_epoch 1500 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.001 --seed 1 --save_path exps/wav/exp2

python trainSpeakerNet.py --model ResNetSE34Multi --modality rgb --noisy_train True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --max_epoch 600 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.01 --seed 1 --save_path exps/rgb/exp2

python trainSpeakerNet.py --model ResNetSE34Multi --modality thr --noisy_train True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --max_epoch 600 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.01 --seed 1 --save_path exps/thr/exp2

Multimodal models

python trainSpeakerNet.py --model ResNetSE34Multi --modality wavrgb --noisy_train True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --max_epoch 600 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.01 --seed 1 --save_path exps/wavrgb/exp2

python trainSpeakerNet.py --model ResNetSE34Multi --modality wavrgbthr --noisy_train True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --max_epoch 600 --batch_size 100 --nPerSpeaker 9 --max_frames 200 --eval_frames 200 --weight_decay 0.1 --seed 1 --save_path exps/wavrgb/exp2

Evaluating pretrained models: clean test data

Unimodal models

python trainSpeakerNet.py --model ResNetSE34Multi --modality wav --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/wav/exp1

python trainSpeakerNet.py --model ResNetSE34Multi --modality rgb --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt   --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/rgb/exp1

python trainSpeakerNet.py --model ResNetSE34Multi --modality thr --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt   --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/thr/exp1

Multimodal models

python trainSpeakerNet.py --model ResNetSE34Multi --modality wavrgb  --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt   --log_input True  --trainfunc angleproto --eval_frames 200 --save_path exps/wavrgb/exp1

python trainSpeakerNet.py --model ResNetSE34Multi --modality wavrgbthr --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt   --log_input True  --trainfunc angleproto --eval_frames 200 --save_path exps/wavrgb/exp1

Evaluating pretrained models: noisy test data

Unimodal models

python revalidate.py --model ResNetSE34Multi --modality wav --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/wav/exp2

python revalidate.py --model ResNetSE34Multi --modality wav --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt    --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/wav/exp2

python revalidate.py --model ResNetSE34Multi --modality rgb --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/rgb/exp2

python revalidate.py --model ResNetSE34Multi --modality rgb --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt    --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/rgb/exp2

python revalidate.py --model ResNetSE34Multi --modality thr --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/thr/exp2

python revalidate.py --model ResNetSE34Multi --modality thr --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt    --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/thr/exp2

Multimodal models

python revalidate.py --model ResNetSE34Multi --modality wavrgb --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/wavrgb/exp2

python revalidate.py --model ResNetSE34Multi --modality wavrgb --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt    --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/wavrgb/exp2

python revalidate.py --model ResNetSE34Multi --modality wavrgbthr --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/wavrgbthr/exp2

python revalidate.py --model ResNetSE34Multi --modality wavrgbthr --eval True --valid_model True --test_path data/test --test_list data/metadata/test_list.txt    --noisy_eval True --p_noise 0.3 --snr 8 --log_input True --trainfunc angleproto --eval_frames 200 --save_path exps/wavrgb/exp2

This repository contains code and data for "On the Multimodal Person Verification Using Audio-Visual-Thermal Data"

Related tags

Overview

trimodal_person_verification

Dependencies

Dataset

Training examples : clean data

Training examples : noisy data

Evaluating pretrained models: clean test data

Evaluating pretrained models: noisy test data

Owner

ISSAI

Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021

Provide partial dates and retain the date precision through processing

Covid19-Forecasting - An interactive website that tracks, models and predicts COVID-19 Cases

Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes

An open-source, low-cost, image-based weed detection device for fallow scenarios.

Contextual Attention Network: Transformer Meets U-Net

This is a computer vision based implementation of the popular childhood game 'Hand Cricket/Odd or Even' in python

A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!

Code for the CVPR2021 workshop paper "Noise Conditional Flow Model for Learning the Super-Resolution Space"

CLIPort: What and Where Pathways for Robotic Manipulation

ML powered analytics engine for outlier detection and root cause analysis.

SSPNet: Scale Selection Pyramid Network for Tiny Person Detection from UAV Images.

Prompt Tuning with Rules

Generative code template for PixelBeasts 10k NFT project.

A nutritional label for food for thought.

Release of the ConditionalQA dataset

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Code for "Multi-Time Attention Networks for Irregularly Sampled Time Series", ICLR 2021.

Learning Features with Parameter-Free Layers (ICLR 2022)

Large scale embeddings on a single machine.