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Like by smiling? Facebook acquires emotion detection startup FacioMetrics

#artificialintelligence

Facebook could one day build facial gesture controls for its app thanks to the acquisition of a Carnegie Mellon University spinoff company called FacioMetrics. The startup made an app called Intraface that could detect seven different emotions in people's faces, but it's been removed from the app stores. The acquisition aligns with a surprising nugget of information Facebook slipped into a 32-bullet point briefing sent to TechCrunch this month. "Future applications of deep learning platform on mobile: Gesture-based controls, recognize facial expressions and perform related actions" It's not hard to imagine Facebook one day employing FacioMetrics' tech and its own AI to let you add a Like or one of its Wow/Haha/Angry/Sad emoji reactions by showing that emotion with your face. "How people share and communicate is changing and things like masks and other effects allow people to express themselves in fun and creative ways.


Let's face-off on Facebook

#artificialintelligence

Who knows in future you may be able to emote to your friend's FB posts in your own wide mouthed haha, open mouthed wow or a puckered brow frown? Going by the indications, things are inching towards such a reality. Facebook's latest acquisition – of a face recognition company FacioMetrics -- has become the talk of the tech town regarding the possibilities of inclusion of facial gesture controls on the app front. Close on the heels of this merger, this startup's apps have been withdrawn from the App Store and Play Store. 'Intraface', the facial image analysis app from Faciometrics could enable detection of seven facial emotions.


Android phones can now read books, signs, business cards via Google's Mobile Vision

ZDNet

Google's Mobile Vision now gains the ability to read text. Google has introduced a new Text API for its Mobile Vision framework that allows Android developers to integrate optical-character recognition (OCR) into their apps. The new Text API appears in the recently-updated Google Play Services version 9.2, which restores Mobile Vision, Google's system to make it easy for developers to add facial detection and barcode-reading functionality to Android apps. The Text OCR technology currently can recognize text in any Latin-based language, covering most European languages, including English, German, and French, as well as Turkish. Google has added Word Lens, a technology acquired last year, to its Google Translate app.


A survey of advances in vision-based vehicle re-identification

arXiv.org Artificial Intelligence

Vehicle re-identification (V-reID) has become significantly popular in the community due to its applications and research significance. In particular, the V-reID is an important problem that still faces numerous open challenges. This paper reviews different V-reID methods including sensor based methods, hybrid methods, and vision based methods which are further categorized into hand-crafted feature based methods and deep feature based methods. The vision based methods make the V-reID problem particularly interesting, and our review systematically addresses and evaluates these methods for the first time. We conduct experiments on four comprehensive benchmark datasets and compare the performances of recent hand-crafted feature based methods and deep feature based methods. We present the detail analysis of these methods in terms of mean average precision (mAP) and cumulative matching curve (CMC). These analyses provide objective insight into the strengths and weaknesses of these methods. We also provide the details of different V-reID datasets and critically discuss the challenges and future trends of V-reID methods.


Text Mining Support in Semantic Annotation and Indexing of Multimedia Data

AAAI Conferences

This short paper is describing a demonstrator that is complementing the paper "Towards Cross-Media Feature Extraction" in these proceedings. The demo is exemplifying the use of textual resources, out of which semantic information can be extracted, for supporting the semantic annotation and indexing of associated video material in the soccer domain. Entities and events extracted from textual data are marked-up with semantic classes derived from an ontology modeling the soccer domain. We show further how extracted Audio-Video features by video analysis can be taken into account for additional annotation of specific soccer event types, and how those different types of annotation can be combined.