Extraction and selection of muscle based features for facial expression recognition

In this study we propose a new set of muscle activity based features for facial expression recognition. We extract muscular activities by observing the displacements of facial feature points in an expression video. The facial feature points are initialized on muscular regions of influence in the first frame of the video. These points are tracked through optical flow in sequential frames. Displacements of feature points on the image plane are used to estimate the 3D orientation of a head model and relative displacements of its vertices. We model the human skin as a linear system of equations. The estimated deformation of the wireframe model produces an over-determined system of equations that can be solved under the constraint of the facial anatomy to obtain muscle activation levels. We apply sequential forward feature selection to choose the most descriptive set of muscles for recognition of basic facial expressions.Publisher's VersionAuthor Post Prin

Brain asymmetry and facial attractiveness: Facial beauty is not simply in the eye of the beholder.

We recently reported finding asymmetry in the appearance of beauty on the face [39]. Here we investigated whether facial beauty is a stable characteristic (on the owner's very face) or is in the perceptual space of the observer. We call the question 'the owner versus observer hypothesis'. We compared identity judgements and attractiveness ratings of observers. Subjects viewed left-left and right-right composites of faces and decided which most resembled the normal face (Experiment 1). Identity judgements (resemblance) are known to be associated with perceptual factors in the observer. Another group viewed the same normal faces and rated them on attractiveness (Experiment 2). In each experiment there were two separate viewing conditions, original and reversed (mirror-image). Lateral reversal did affect the results of Experiment 1 (confirming previous findings [3,18]) but did not affect the results of Experiment 2. The fact that lateral reversal did not affect the results of Experiment 2 suggests that facial attractiveness is more dependent on physiognomy (of the owner) and less dependent on an asymmetrical perceptual process (in the observer) than is facial identity. The results are discussed in the context of beauty’s biological significance and facial processing in the brain

Constrained Deep Transfer Feature Learning and its Applications

Feature learning with deep models has achieved impressive results for both data representation and classification for various vision tasks. Deep feature learning, however, typically requires a large amount of training data, which may not be feasible for some application domains. Transfer learning can be one of the approaches to alleviate this problem by transferring data from data-rich source domain to data-scarce target domain. Existing transfer learning methods typically perform one-shot transfer learning and often ignore the specific properties that the transferred data must satisfy. To address these issues, we introduce a constrained deep transfer feature learning method to perform simultaneous transfer learning and feature learning by performing transfer learning in a progressively improving feature space iteratively in order to better narrow the gap between the target domain and the source domain for effective transfer of the data from the source domain to target domain. Furthermore, we propose to exploit the target domain knowledge and incorporate such prior knowledge as a constraint during transfer learning to ensure that the transferred data satisfies certain properties of the target domain. To demonstrate the effectiveness of the proposed constrained deep transfer feature learning method, we apply it to thermal feature learning for eye detection by transferring from the visible domain. We also applied the proposed method for cross-view facial expression recognition as a second application. The experimental results demonstrate the effectiveness of the proposed method for both applications.Comment: International Conference on Computer Vision and Pattern Recognition, 201