Applying Virtual Makeup Using Makeup Detection and Recommendations

This publication describes systems and techniques for makeup detection on an electronic device that uses an image of a user’s desired look as an input to detect makeup. The detected makeup is mapped to a virtual makeup library and saved as virtual makeup in a corresponding user profile. The user can retrieve and apply the virtual makeup to their face in another image to achieve a desired look. The mapped virtual makeup can be displayed as a filter over an image to digitally create an appearance of the user wearing makeup. The user is able to adjust the strength, color, and/or style of the virtual makeup. Further, the user may be presented with one or more recommendations for virtual makeup based on attributes of an image (e.g., a background of an image, the user’s clothing, hairstyle, etc.). The recommendations for virtual makeup may be based on results of a machine-learned model that received training from a professional source (e.g., stylist, makeup artist, etc.). The recommendations for virtual makeup may display on a captured image or they may display in real time on a display of the electronic device. The user is able to adjust the strength, color, and/or style of the recommendations for virtual makeup to achieve a desired look

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

On the effect of age perception biases for real age regression

Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels benefit real age estimation, rather than direct real to real age regression, our main contribution is the integration, in an end-to-end architecture, of face attributes for apparent age prediction with an additional loss for real age regression. Experimental results on the APPA-REAL dataset indicate the proposed network successfully take advantage of the adopted attributes to improve both apparent and real age estimation. Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression. Finally, we present preliminary results and discussion of a proof of concept application using the proposed model to regress the apparent age of an individual based on the gender of an external observer.Comment: Accepted in the 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019

Detecting Interesting Events in a Home Security Camera System

Generally, the present disclosure is directed to a system for predicting whether the subject of a camera needs to be recorded and/or transmitted. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict whether the view of the camera contains a noteworthy change in semantic meaning based on labels describing the semantic meaning of the view