Publication

Leveraging Convolutional Pose Machines for Fast and Accurate Head Pose Estimation

Jean-Marc Odobez, Olivier Canévet
2018
Conference Papers

Abstract

We propose a head pose estimation framework that leverages on a recent keypoint detection model. More specifically, we apply the convolutional pose machines (CPMs) to input images, extract different types of facial keypoint features capturing appearance information and keypoint relationships, and train multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) for head pose estimation. The benefit of leveraging on the CPMs (which we apply anyway for other purposes like tracking) is that we can design highly efficient models for practical usage. We evaluate our approach on the Annotated Facial Landmarks in the Wild (AFLW) dataset and achieve competitive results with the state-of-the-art.

Official source

https://infoscience.epfl.ch/entities/publication/9a4a45a8-9192-442b-ac2b-a7dc3a772b2b

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Jean-Marc Odobez, Olivier Canévet
2018
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/9a4a45a8-9192-442b-ac2b-a7dc3a772b2b

About this result

Related concepts (24)

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Computer vision

Computer vision tasks include methods for , , and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input to the retina in the human analog) into descriptions of the world that make sense to thought processes and can elicit appropriate action.

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