Distance Estimation of an Unknown Person from a Portrait

We propose the first automated method for estimating distance from frontal pictures of unknown faces. Camera calibration is not necessary, nor is the reconstruction of a 3D representation of the shape of the head. Our method is based on estimating automatically the position of face and head landmarks in the image, and then using a regressor to estimate distance from such measurements. We collected and annotated a dataset of frontal portraits of 53 individuals spanning a number of attributes (sex, age, race, hair), each photographed from seven distances. We find that our proposed method outperforms humans performing the same task. We observe that different physiognomies will bias systematically the estimate of distance, i.e. some people look closer than others. We expire which landmarks are more important for this task

Vision for Social Robots: Human Perception and Pose Estimation

In order to extract the underlying meaning from a scene captured from the surrounding world in a single still image, social robots will need to learn the human ability to detect different objects, understand their arrangement and relationships relative both to their own parts and to each other, and infer the dynamics under which they are evolving. Furthermore, they will need to develop and hold a notion of context to allow assigning different meanings (semantics) to the same visual configuration (syntax) of a scene. The underlying thread of this Thesis is the investigation of new ways for enabling interactions between social robots and humans, by advancing the visual perception capabilities of robots when they process images and videos in which humans are the main focus of attention. First, we analyze the general problem of scene understanding, as social robots moving through the world need to be able to interpret scenes without having been assigned a specific preset goal. Throughout this line of research, i) we observe that human actions and interactions which can be visually discriminated from an image follow a very heavy-tailed distribution; ii) we develop an algorithm that can obtain a spatial understanding of a scene by only using cues arising from the effect of perspective on a picture of a person’s face; and iii) we define a novel taxonomy of errors for the task of estimating the 2D body pose of people in images to better explain the behavior of algorithms and highlight their underlying causes of error. Second, we focus on the specific task of 3D human pose and motion estimation from monocular 2D images using weakly supervised training data, as accurately predicting human pose will open up the possibility of richer interactions between humans and social robots. We show that when 3D ground-truth data is only available in small quantities, or not at all, it is possible to leverage knowledge about the physical properties of the human body, along with additional constraints related to alternative types of supervisory signals, to learn models that can regress the full 3D pose of the human body and predict its motions from monocular 2D images. Taken in its entirety, the intent of this Thesis is to highlight the importance of, and provide novel methodologies for, social robots' ability to interpret their surrounding environment, learn in a way that is robust to low data availability, and generalize previously observed behaviors to unknown situations in a similar way to humans.</p

What Does 2D Geometric Information Really Tell Us About 3D Face Shape?

A face image contains geometric cues in the form of configurational information (semantically meaningful landmark points and contours). In this thesis, we explore to what degree such 2D geometric information allows us to estimate 3D face shape. First, we focus on the problem of fitting a 3D morphable model to single face images using only sparse geometric features. We propose a novel approach that explicitly computes hard correspondences which allow us to treat the model edge vertices as known 2D positions, for which optimal pose or shape estimates can be linearly computed. Moreover, we show how to formulate this shape-from-landmarks problem as a separable nonlinear least squares optimisation. Second, we show how a statistical model can be used to spatially transform input data as a module within a convolutional neural network. This is an extension of the original spatial transformer network in that we are able to interpret and normalise 3D pose changes and self-occlusions. We show that the localiser can be trained using only simple geometric loss functions on a relatively small dataset yet is able to perform robust normalisation on highly uncontrolled images. We consider another extension in which the model itself is also learnt. The final contribution of this thesis lies in exploring the limits of 2D geometric features and characterising the resulting ambiguities. 2D geometric information only provides a partial constraint on 3D face shape. In other words, face landmarks or occluding contours are an ambiguous shape cue. Two faces with different 3D shape can give rise to the same 2D geometry, particularly as a result of perspective transformation when camera distance varies. We derive methods to compute these ambiguity subspaces, demonstrate that they contain significant shape variability and show that these ambiguities occur in real-world datasets

Distance Estimation of an Unknown Person from a Portrait

We propose the first automated method for estimating distance from frontal pictures of unknown faces. Camera calibration is not necessary, nor is the reconstruction of a 3D representation of the shape of the head. Our method is based on estimating automatically the position of face and head landmarks in the image, and then using a regressor to estimate distance from such measurements. We collected and annotated a dataset of frontal portraits of 53 individuals spanning a number of attributes (sex, age, race, hair), each photographed from seven distances. We find that our proposed method outperforms humans performing the same task. We observe that different physiognomies will bias systematically the estimate of distance, i.e. some people look closer than others. We expire which landmarks are more important for this task

Caltech Multi-Distance Portraits (CMDP)

A dataset of frontal portraits of 53 individuals spanning a number of attributes (sex, age, race, hair), each photographed from seven distances.Related Publication: Distance Estimation of an Unknown Person from a Portrait Burgos-Artizzu, Xavier Ronchi, Matteo Ruggero Perona, Pietro Proceedings of the European Conference on Computer Vision (ECCV) 2014-09-02 https://doi.org/10.1007/978-3-319-10590-1_21 en