Development of face analysis approximation methods by machine learning. (Développement de méthodes de rapprochement physionomique par apprentissage machine)

The work presented in this PhD thesis takes place in the general context of face matching. More precisely, our goal is to design and develop novel algorithms to learn compact, discriminative, domain invariant or de-identifying representations of faces. Searching and indexing faces open the door to many interesting applications. However, this is made day after day more challenging due to the rapid growth of the volume of faces to analyse. Representing faces by compact and discriminative features is consequently essential to deal with such very large datasets. Moreover, this volume is increasing without any apparent limits; this is why it is also relevant to propose solutions to organise faces in meaningful ways, in order to reduce the search space and improve efficiency of the retrieval. Although the volume of faces available on the internet is increasing, it is still difficult to find annotated examples to train models for each possible use cases e.g. for different races, sexes, etc. for every specific task. Learning a model with training examples from a group of people can fail to predict well in another group due to the uneven rate of changes of biometric dimensions e.g., ageing, among them. Similarly, a model learned from a type of feature can fail to make good predictions when tested with another type of feature. It would be ideal to have models producing face representations that would be invariant to these discrepancies. Learning common representations ultimately helps to reduce the domain specific parameters and, more importantly, allows to use training examples from domains well represented to other domains. Hence, there is a need for designing algorithms to map the features from different domains to a common subspace – bringing faces bearing same properties closer. On the other hand, as automatic face matching tools are getting smarter and smarter, there is an increasing threat on privacy. The popularity in photo sharing on the social networks has exacerbated this risk. In such a context, altering the representations of faces so that the faces cannot be identified by automatic face matchers -- while the faces look as similar as before -- has become an interesting perspective toward privacy protection. It allows users to limit the risk of sharing their photos in social networks. In all these scenarios, we explored how the use of Metric Learning methods as well as those of Deep Learning can help us to learn compact and discriminative representations of faces. We build on these tools, proposing compact, discriminative, domain invariant representations and de-identifying representations of faces. We applied the proposed methods on a wide range of facial analysing applications. These applications include: large-scale face retrieval, age estimation, attribute predictions and identity de-identification. We have evaluated our algorithms on standard and challenging public datasets such as: LFW, CelebA, MORPH II etc. Moreover, we appended 1M faces crawled from Flicker.com to LFW and generated a novel and more challenging dataset to evaluate our algorithms in large-scale. Our experiments show that the proposed methods are more accurate and more efficient than compared competitive baselines and existing state-of-art methods, and attain new state-of-art performance.