PLM-IPE: A Pixel-Landmark Mutual Enhanced Framework for Implicit Preference Estimation

In this paper, we are interested in understanding how customers perceive fashion recommendations, in particular when observing a proposed combination of garments to compose an outfit. Automatically understanding how a suggested item is perceived, without any kind of active engagement, is in fact an essential block to achieve interactive applications. We propose a pixel-landmark mutual enhanced framework for implicit preference estimation, named PLM-IPE, which is capable of inferring the user's implicit preferences exploiting visual cues, without any active or conscious engagement. PLM-IPE consists of three key modules: pixel-based estimator, landmark-based estimator and mutual learning based optimization. The former two modules work on capturing the implicit reaction of the user from the pixel level and landmark level, respectively. The last module serves to transfer knowledge between the two parallel estimators. Towards evaluation, we collected a real-world dataset, named SentiGarment, which contains 3,345 facial reaction videos paired with suggested outfits and human labeled reaction scores. Extensive experiments show the superiority of our model over state-of-the-art approaches

Inner Eye Canthus Localization for Human Body Temperature Screening

In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nosetip and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows to obtain the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose and mouth, and are not specifically tailored for localizing the eye canthus region. As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions. The data will be publicly released

A Dictionary Learning based 3D Morphable Shape Model

Face analysis from 2D images and videos is a central task in many multimedia applications. Methods developed to this end perform either face recognition or facial expression recognition, and in both cases results are negatively influenced by variations in pose, illumination and resolution of the face. Such variations have a lower impact on 3D face data, which has given the way to the idea of using a 3D Morphable Model as an intermediate tool to enhance face analysis on 2D data. In this paper, we propose a new approach for constructing a 3D Morphable Shape Model (called DL-3DMM) and show our solution can reach the accuracy of deformation required in applications where fine details of the face are concerned. For constructing the model, we start from a set of 3D face scans with large variability in terms of ethnicity and expressions. Across these training scans, we compute a point-to-point dense alignment, which is accurate also in the presence of topological variations of the face. The DL-3DMM is constructed by learning a dictionary of basis components on the aligned scans. The model is then fitted to 2D target faces using an efficient regularized ridge-regression guided by 2D/3D facial landmark correspondences in order to generate pose-normalized face images. Comparison between the DL-3DMM and the standard PCA-based 3DMM demonstrates that in general a lower reconstruction error can be obtained with our solution. Application to action unit detection and emotion recognition from 2D images and videos shows competitive results with state of the art methods on two benchmark datasets