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

A review of content-based video retrieval techniques for person identification

The rise of technology spurs the advancement in the surveillance field. Many commercial spaces reduced the patrol guard in favor of Closed-Circuit Television (CCTV) installation and even some countries already used surveillance drone which has greater mobility. In recent years, the CCTV Footage have also been used for crime investigation by law enforcement such as in Boston Bombing 2013 incident. However, this led us into producing huge unmanageable footage collection, the common issue of Big Data era. While there is more information to identify a potential suspect, the massive size of data needed to go over manually is a very laborious task. Therefore, some researchers proposed using Content-Based Video Retrieval (CBVR) method to enable to query a specific feature of an object or a human. Due to the limitations like visibility and quality of video footage, only certain features are selected for recognition based on Chicago Police Department guidelines. This paper presents the comprehensive reviews on CBVR techniques used for clothing, gender and ethnic recognition of the person of interest and how can it be applied in crime investigation. From the findings, the three recognition types can be combined to create a Content-Based Video Retrieval system for person identification