From Hard to Soft Biometrics Through DNN Transfer Learning

Transfer learning is a paradigm that consists in training and testing classifiers with datasets drawn from distinct distributions. This technique allows to solve a particular problem using a model that was trained for another purpose. In the recent years, this practice has become very popular due to the increase of public available pre-trained models that can be fine-tuned to be applied in different scenarios. However, the relationship between the datasets used for training the model and the test data is usually not addressed, specially where the fine-tuning process is done only for the fully connected layers of a Convolutional Neural Network with pre-trained weights. This work presents a study regarding the relationship between the datasets used in a transfer learning process in terms of the performance achieved by models complexities and similarities. For this purpose, we fine-tune the final layer of Convolutional Neural Networks with pre-trained weights using diverse soft biometrics datasets. An evaluation of the performances of the models, when tested with datasets that are different from the one used for training the model, is presented. Complexity and similarity metrics are also used to perform the evaluation.

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Soft Biometrics Classication in Videos Using Transfer Learning and Bidirectional Long Short-Term Memory Networks

Learning and Nonlinear Models ◽

10.21528/lnlm-vol18-no1-art4 ◽

2020 ◽

Vol 18 (1) ◽

pp. 47-59

Author(s):

Marcelo Romero ◽

Matheusq Gutoski ◽

Leandro Takeshi Hattori ◽

Manassés Ribeiro ◽

Heitor S. Lopes

Keyword(s):

Transfer Learning ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Soft Biometrics ◽

Long Short Term Memory

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Soft Biometrics and Deep Learning: Detecting Facial Soft Biometrics Features Using Ocular and Forehead Region for Masked Face Images

10.21203/rs.3.rs-1174842/v1 ◽

2022 ◽

Author(s):

Urja Banati ◽

Vamika Prakash ◽

Rashi Verma ◽

Smriti Srivast

Keyword(s):

Transfer Learning ◽

Recognition System ◽

Fingerprint Recognition ◽

Data Sets ◽

Soft Biometrics ◽

Learning Techniques ◽

Hair Type ◽

The Face ◽

And Gender ◽

Improving Accuracy

Abstract Soft Biometrics is a growing field that has been known to improve the recognition system as witnessed in the past decade. When combined with hard biometrics like iris, gait, fingerprint recognition etc. it has been seen that the efficiency of the system increases many folds. With the Pandemic came the need to recognise faces covered with mask in an efficient way- soft biometrics proved to be an aid in this. While recent advances in computer vision have helped in the estimation of age and gender - the system could be improved by extending the scope and detecting quite a few other soft biometric attributes that helps us in identifying a person, including but not limited to - eyeglasses, hair type and color, mustache, eyebrows etc. In this paper we propose a system of identification that uses the ocular and forehead part of the face as modalities to train our models that uses transfer learning techniques to help in the detection of 12 soft biometric attributes (FFHQ dataset) and 25 soft biometric attributes (CelebA dataset) for masked faces. We compare the results with the unmasked faces in order to see the variation of efficiency using these data-sets Throughout the paper we have implemented 4 enhanced models namely - enhanced Alexnet ,enhanced Resnet50, enhanced MobilenetV2 and enhanced SqueezeNet. The enhanced models apply transfer learning to the normal models and aids in improving accuracy. In the end we compare the results and see how the accuracy varies according to the model used and whether the images are masked or unmasked. We conclude that for images containing facial masks - using enhanced MobileNet would give a splendid accuracy of 92.5% (for FFHQ dataset) and 87% (for CelebA dataset).

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Indices of transfer: Learning can transfer but still be specific

PsycEXTRA Dataset ◽

10.1037/e520592012-493 ◽

2010 ◽

Author(s):

Erica L. Wohldmann ◽

Alice F. Healy

Keyword(s):

Transfer Learning

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A transfer learning based microstructure reconstruction approach using microstructural correlation functions

10.26226/morressier.5f5f8e69aa777f8ba5bd604c ◽

2020 ◽

Author(s):

Ashwini Gupta

Keyword(s):

Transfer Learning ◽

Correlation Functions ◽

Microstructure Reconstruction

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Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

10.26434/chemrxiv.9913865.v2 ◽

2019 ◽

Author(s):

Qi Yuan ◽

Alejandro Santana-Bonilla ◽

Martijn Zwijnenburg ◽

Kim Jelfs

Keyword(s):

Neural Network ◽

Electronic Properties ◽

Transfer Learning ◽

Recurrent Neural Network ◽

Chemical Space ◽

Generative Models ◽

Molecular Features ◽

Donor Acceptor ◽

Homo Lumo ◽

Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

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Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning

10.3115/v1/w14-4202 ◽

2014 ◽

Author(s):

Hiroshi Kanayama ◽

Youngja Park ◽

Yuta Tsuboi ◽

Dongmook Yi

Keyword(s):

Transfer Learning

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Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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