Facebook acquires facial image analysis startup FacioMetrics

PCWorld

Facebook has acquired a facial image analysis firm FacioMetrics as it tries to give users new features to add special effects to photos and videos. The technology developed by the startup also includes capabilities for face tracking and recognizing emotions, which could potentially open up other applications for Facebook. The financial terms of the acquisition of FacioMetrics, a startup that was spun off from Carnegie Mellon University, were not disclosed. Facebook will discontinue the products, which are no longer available on app stores.The FacioMetrics website now only has a message about the acquisition. "How people share and communicate is changing and things like masks and other effects allow people to express themselves in fun and creative ways," a Facebook spokesman wrote in an email Wednesday.


Facebook buys facial recognition tech startup

Daily Mail - Science & tech

Soon you could'like' a photo with a SMILE: Facebook buys facial recognition firm to compete with Snapchat FacioMetrics' software analyses a person's facial features and expressions Using recognition and analysis could enable Facebook to rival Snapchat Facebook says it hopes the software will bring'more fun effects' to users' photos and videos Facebook says it hopes the software will bring'more fun effects' to users' photos and videos Mark Zuckerberg hacked AGAIN: Facebook founder's accounts... Facebook unveils new'transparent' metrics site as it admits... Goodbye trolls! Twitter adds the ability to mute unwelcome... Is YOUR phone safe? Mark Zuckerberg hacked AGAIN: Facebook founder's accounts... Facebook unveils new'transparent' metrics site as it admits... Goodbye trolls! Twitter adds the ability to mute unwelcome... Is YOUR phone safe? Facebook could soon be looking right back at you.


What Makes Automatic Emotion Detection So Powerful?

#artificialintelligence

The one and only reason why businesses are turning to automatic emotion detection is you! Emotion sensing technologies are expanding exponentially. Market researchers estimate the Emotion Detection & Recognition (EDR) business to grow at a compound annual growth rate (CAGR) of 27.20–39.9%, One of the most common ways to automatically recognize emotions is via facial detection in photos and videos. The list of softwares or APIs that allow you to do that keeps on getting longer.


It All Matters: Reporting Accuracy, Inference Time and Power Consumption for Face Emotion Recognition on Embedded Systems

arXiv.org Machine Learning

While several approaches to face emotion recognition task are proposed in literature, none of them reports on power consumption nor inference time required to run the system in an embedded environment. Without adequate knowledge about these factors it is not clear whether we are actually able to provide accurate face emotion recognition in the embedded environment or not, and if not, how far we are from making it feasible and what are the biggest bottlenecks we face. The main goal of this paper is to answer these questions and to convey the message that instead of reporting only detection accuracy also power consumption and inference time should be reported as real usability of the proposed systems and their adoption in human computer interaction strongly depends on it. In this paper, we identify the state-of-the art face emotion recognition methods that are potentially suitable for embedded environment and the most frequently used datasets for this task. Our study shows that most of the performed experiments use datasets with posed expressions or in a particular experimental setup with special conditions for image collection. Since our goal is to evaluate the performance of the identified promising methods in the realistic scenario, we collect a new dataset with non-exaggerated emotions and we use it, in addition to the publicly available datasets, for the evaluation of detection accuracy, power consumption and inference time on three frequently used embedded devices with different computational capabilities. Our results show that gray images are still more suitable for embedded environment than color ones and that for most of the analyzed systems either inference time or energy consumption or both are limiting factor for their adoption in real-life embedded applications.


Amazon Says It Can Detect Fear on Your Face. You Scared?

#artificialintelligence

Amazon announced a breakthrough from its AI experts Monday: Their algorithms can now read fear on your face, at a cost of $0.001 per image--or less if you process more than 1 million images. The news sparked interest because Amazon is at the center of a political tussle over the accuracy and regulation of facial recognition. Amazon sells a facial-recognition service, part of a suite of image-analysis features called Rekognition, to customers that include police departments. Another Rekognition service tries to discern the gender of faces in photos. The company said Monday that the gender feature had been improved--apparently a response to research showing it was much less accurate for people with darker skin.