leoputera2407 / missing_child

A Machine Learning Project to detect kinship relationship to help missing children re-unite with their parents

What is our project?

We aim to reunite children with their parents after disaster. By equipping every shelter / disaster relief site with a node in the network and a camera, we use kinship verification technology to match children with their mother and father.

Hypothesis

The hypothesis of this project is that the age-invariant features of children are inherited from parents. By inferring from these features, we may tell whether a child is related to a father-mother pair.

Architecture

We utilize transfer learning by extracting age-invariant features from the mother, father and child. We learn a comparison function by the use of fully-connected and concatenation layer. Our approach is to compare the father and child, and the mother and child. Then, these comparisons are concatenated, and are inferred from to obtain a final relatedness score (0-1).

results

Date	Dataset	Model	Validation Acc	Validation Loss	Training Acc	starting LR	Epochs	Batch size	Batches per epoch	Notes	Weights filename
Sep 5 2019	FIW	model_v3	90.26%	0.389	91.37%	0.0001	2410	50 F-M-C(+)-C(-) pairs	20	Reduced LR on plateau after a patience of 75 epochs	src/ml/saved_model_weights/v3_weights.2409-0.39.hdf5
Sep 1 2019	FIW	model_v3	86.57%	0.495	99.05%	0.0001	2134	50 F-M-C(+)-C(-) pairs	20	Reduced LR on plateau after a patience of 75 epochs	src/ml/saved_model_weights/v3_weights.2134-0.50.hdf5

Note: to use the model weights, you must point the model saver (see train_model_v3.py) to the location of the weights.

TODO

Things we need to do:

• Implement more loss functions if needed (e.g. crossentropy?)
• Visualize using tensorboard.
• Finish train_model.py and model.py
• Implement data loader class to load images from FIW / other dataset
• Implement algorithm to sample (MF-C) pairs used in triplet pairs
• Fix training bug in tensorflow - (multiple forward passes result in weight update)
• Try reducing LR on plateau after 80 epochs

Papers used:

Look Across Elapse

 @article{zhao2018look,
      title={Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition},
      author={Zhao, Jian and Cheng, Yu and Cheng, Yi and Yang, Yang and Lan, Haochong and Zhao, Fang and Xiong, Lin and Xu, Yan and Li, Jianshu and Pranata, Sugiri and others},
      journal={AAAI},
      year={2019}
      }