Face Alignment Assisted by Head Pose Estimation

In this paper we propose a supervised initialization scheme for cascaded face alignment based on explicit head pose estimation. We first investigate the failure cases of most state of the art face alignment approaches and observe that these failures often share one common global property, i.e. the head pose variation is usually large. Inspired by this, we propose a deep convolutional network model for reliable and accurate head pose estimation. Instead of using a mean face shape, or randomly selected shapes for cascaded face alignment initialisation, we propose two schemes for generating initialisation: the first one relies on projecting a mean 3D face shape (represented by 3D facial landmarks) onto 2D image under the estimated head pose; the second one searches nearest neighbour shapes from the training set according to head pose distance. By doing so, the initialisation gets closer to the actual shape, which enhances the possibility of convergence and in turn improves the face alignment performance. We demonstrate the proposed method on the benchmark 300W dataset and show very competitive performance in both head pose estimation and face alignment.Comment: Accepted by BMVC201

Novel Tactile-SIFT Descriptor for Object Shape Recognition

Using a tactile array sensor to recognize an object often requires multiple touches at different positions. This process is prone to move or rotate the object, which inevitably increases difficulty in object recognition. To cope with the unknown object movement, this paper proposes a new tactile-SIFT descriptor to extract features in view of gradients in the tactile image to represent objects, to allow the features being invariant to object translation and rotation. The tactile-SIFT segments a tactile image into overlapping subpatches, each of which is represented using a dn-dimensional gradient vector, similar to the classic SIFT descriptor. Tactile-SIFT descriptors obtained from multiple touches form a dictionary of k words, and the bag-of-words method is then used to identify objects. The proposed method has been validated by classifying 18 real objects with data from an off-the-shelf tactile sensor. The parameters of the tactile-SIFT descriptor, including the dimension size dn and the number of subpatches sp, are studied. It is found that the optimal performance is obtained using an 8-D descriptor with three subpatches, taking both the classification accuracy and time efficiency into consideration. By employing tactile-SIFT, a recognition rate of 91.33% has been achieved with a dictionary size of 50 clusters using only 15 touches