Gender and gaze gesture recognition for human-computer interaction

© 2016 Elsevier Inc. The identification of visual cues in facial images has been widely explored in the broad area of computer vision. However theoretical analyses are often not transformed into widespread assistive Human-Computer Interaction (HCI) systems, due to factors such as inconsistent robustness, low efficiency, large computational expense or strong dependence on complex hardware. We present a novel gender recognition algorithm, a modular eye centre localisation approach and a gaze gesture recognition method, aiming to escalate the intelligence, adaptability and interactivity of HCI systems by combining demographic data (gender) and behavioural data (gaze) to enable development of a range of real-world assistive-technology applications. The gender recognition algorithm utilises Fisher Vectors as facial features which are encoded from low-level local features in facial images. We experimented with four types of low-level features: greyscale values, Local Binary Patterns (LBP), LBP histograms and Scale Invariant Feature Transform (SIFT). The corresponding Fisher Vectors were classified using a linear Support Vector Machine. The algorithm has been tested on the FERET database, the LFW database and the FRGCv2 database, yielding 97.7%, 92.5% and 96.7% accuracy respectively. The eye centre localisation algorithm has a modular approach, following a coarse-to-fine, global-to-regional scheme and utilising isophote and gradient features. A Selective Oriented Gradient filter has been specifically designed to detect and remove strong gradients from eyebrows, eye corners and self-shadows (which sabotage most eye centre localisation methods). The trajectories of the eye centres are then defined as gaze gestures for active HCI. The eye centre localisation algorithm has been compared with 10 other state-of-the-art algorithms with similar functionality and has outperformed them in terms of accuracy while maintaining excellent real-time performance. The above methods have been employed for development of a data recovery system that can be employed for implementation of advanced assistive technology tools. The high accuracy, reliability and real-time performance achieved for attention monitoring, gaze gesture control and recovery of demographic data, can enable the advanced human-robot interaction that is needed for developing systems that can provide assistance with everyday actions, thereby improving the quality of life for the elderly and/or disabled

Human-display interaction technology: Emerging remote interfaces for pervasive display environments

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.We're living in a world where information processing isn't confined to desktop computers - it's being integrated into everyday objects and activities. Pervasive computation is human centered: it permeates our physical world, helping us achieve goals and fulfill our needs with minimum effort by exploiting natural interaction styles. Remote interaction with screen displays requires a sensor-based, multimodal, touchless approach. For example, by processing user hand gestures, this paradigm removes constraints requiring physical contact and permits natural interaction with tangible digital information. Such touchless interaction can be multimodal, exploiting the visual, auditory, and olfactory senses.Ministerio de Educación y Ciencia and Amper Sistemas, SA

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Facial Feature Tracking and Occlusion Recovery in American Sign Language

Facial features play an important role in expressing grammatical information in signed languages, including American Sign Language(ASL). Gestures such as raising or furrowing the eyebrows are key indicators of constructions such as yes-no questions. Periodic head movements (nods and shakes) are also an essential part of the expression of syntactic information, such as negation (associated with a side-to-side headshake). Therefore, identification of these facial gestures is essential to sign language recognition. One problem with detection of such grammatical indicators is occlusion recovery. If the signer's hand blocks his/her eyebrows during production of a sign, it becomes difficult to track the eyebrows. We have developed a system to detect such grammatical markers in ASL that recovers promptly from occlusion. Our system detects and tracks evolving templates of facial features, which are based on an anthropometric face model, and interprets the geometric relationships of these templates to identify grammatical markers. It was tested on a variety of ASL sentences signed by various Deaf native signers and detected facial gestures used to express grammatical information, such as raised and furrowed eyebrows as well as headshakes.National Science Foundation (IIS-0329009, IIS-0093367, IIS-9912573, EIA-0202067, EIA-9809340

Gaze and Gestures in Telepresence: multimodality, embodiment, and roles of collaboration

This paper proposes a controlled experiment to further investigate the usefulness of gaze awareness and gesture recognition in the support of collaborative work at a distance. We propose to redesign experiments conducted several years ago with more recent technology that would: a) enable to better study of the integration of communication modalities, b) allow users to freely move while collaborating at a distance and c) avoid asymmetries of communication between collaborators.Comment: Position paper, International Workshop New Frontiers in Telepresence 2010, part of CSCW2010, Savannah, GA, USA, 7th of February, 2010. http://research.microsoft.com/en-us/events/nft2010