#19.02 - Topological data analysis and gene expression - Face & heart biometrics

• "Information Topological data analysis, condensed view of clusters and complex data structures", Pierre Baudot (Inserm-Unité de Neurobiologie, Faculté de Médecine, Université Aix Marseille)

• "Robust face analysis employing machine learning techniques for remote heart rate estimation and towards unbiased attribute analysis", Abhijit Das (INRIA-Stars)

P. Baudot:
As a domain that formalizes the classification and recognition of patterns-structures in mathematics, Topological Data Analysis has progressively gathered the interest of the data science community. On the side of neural networks following Hinton, Amari, information geometric approaches have provided well defined metric and gradient descent methods. This presentation will focus on an original approach of algebraic topology intrinsically based on probability/statistics and information, developed notably with D. Bennequin since 2006. Information topology characterizes uniquely usual information functions, unraveling that two theories, cohomology and information theory, are of the same nature. These probabilistic tools describe the statistical forms or patterns present in databases and make them correspond to discrete symmetries. The set of statistical interactions-dependencies between k elementary variables is quantified by the multivariate mutual information between these k components. It provides a generalized and metric-free decomposition of free energy that is used in machine learning and artificial intelligence. Its application to gene expression under open source software makes it possible to detect functional modules of covariant variables (collective dynamics) as well as clusters (corresponding to condensation phenomena and negative synergistic interactions) in high dimension, and thus to analyze the structure and to quantify diversity in data or arbitrary complex systems (imagery, omics, social networks, ecosystems …).

Baudot & Bennequin (2015): "The homological nature of entropy", Entropy, 17, 1-66

Baudot, Tapia & Goaillard (2018): "Topological Information Data Analysis: Poincare-Shannon Machine and Statistical Physic of Finite Heterogeneous Systems"

Tapia, Baudot, Dufour, Formizano-Treziny, Temporal, Lasserre, Kobayashi & Goaillard (2018): "Neurotransmitter identity and electrophysiological phenotype are genetically coupled in midbrain dopaminergic neurons", Nature Scientific Reports

http://forum.cs-dc.org/category/72/geometric-science-of-information

Software: https://github.com/pierrebaudot/INFOTOPO

A. Das:
In the last century, automatic face analysis has been a very prominent topic of interest for researchers as it can be employed for identity/attribute classification for security and identity purposes, emotion analysis to understand the mental state of an individual, health monitoring, etc. Owing to the real-life application, face analysis comprises a lot of challenges. This talk will highlight some of the recent computer vision and machine learning techniques to hinge robust face analysis. The talk will address 1) the convolution neural network (CNN)-based attention for remote heart rate estimation, and 2) work on CNN-based multi-tasking techniques for robust face attribute classification.

Das, Dantecheva & Bremond (2018): "Mitigating Bias in Gender, Age, and Ethnicity Classification: a Multi-Task Convolution Neural Network Approach", winner paper of the ECCV'18 challenge on bias estimation in face analysis (BEFA)

Niu, Das &o (2019): "Robust Remote Heart Rate Estimation from Face Videos Utilizing Spatial-temporal Attention"